Data driven systems and methods for optimization of a target business

ABSTRACT

The present disclosure is directed to a system and associated methods for assessing, evaluating a target business, and leveraging insights from the assessment and evaluation to provide strategy recommendations to optimize the performance of the target business. In some embodiments, the system may identify a benchmark competitor and determine a performance score for the benchmark competitor. In some embodiments, the system may determine a domain score of an identified benchmark competitor. In some embodiments, the system determines an updated performance score based on provided capability process data. In some embodiments, the system recommends solutions and/or key performance indicators (KPI) to solve a provided problem. In some embodiments, the system provides a platform to enable stakeholders, (e.g., users) in the target business to perform a design thinking process.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.62/894,400, filed Aug. 30, 2019, the entire text of which is herebyincorporated by reference into this patent application.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to a system and associatedmethods for assessing, evaluating a target business, and leveraginginsights from the assessment and evaluation to provide strategyrecommendations to improve the performance of the target business.

BACKGROUND OF THE DISCLOSURE

In order to maintain a competitive edge, improve efficiency, and gainmarket share, businesses typically identify areas of improvement. Forexample, identifying areas of improvement may include, but are notlimited to, identifying inefficiencies in the lifecycle of a process,identifying individuals that act as a bottleneck to processes,identifying weak key performance indicators (KPIs), and the like.Businesses may sometimes internally identify areas improvement andidentify possible solutions. Other times, businesses may hire costlythird party firms to identify areas of improvement and identify possiblesolutions. Both of these systems (internal and external) are subject tohuman biases. Accordingly, a data driven efficient system foridentifying business problem areas and recommending a path forward wouldbe advantageous to businesses.

SUMMARY OF THE DISCLOSURE

The present disclosure is directed to a system and associated methodsfor assessing, evaluating a target business, and leveraging insightsfrom the assessment and evaluation to provide strategy recommendationsto optimize the performance of the target business. In some embodiments,the system may identify a benchmark competitor and determine aperformance score for the benchmark competitor. In some embodiments, thesystem may determine a domain score of an identified benchmarkcompetitor. In some embodiments, the system determines an updatedperformance score based on provided capability process data. In someembodiments, the system recommends solutions and/or KPI to solve aprovided problem. In some embodiments, the system provides a platform toenable stakeholders, (e.g., users) in the target business to perform adesign thinking process.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system, according to embodiments of thisdisclosure.

FIG. 2 illustrates a system, according to embodiments of thisdisclosure.

FIG. 3 illustrates a flowchart of an exemplary operation of a system,according to embodiments of this disclosure.

FIG. 4 illustrates a flowchart of an exemplary target businessassessment process, according to embodiments of this disclosure.

FIG. 5 illustrates a flowchart of an exemplary competitor assessmentprocess, according to embodiments of this disclosure.

FIG. 6 illustrates a flowchart of an exemplary competitor assessmentprocess, according to embodiments of this disclosure.

FIG. 7 illustrates an exemplary data, according to embodiments of thisdisclosure.

FIG. 8 illustrates a flowchart of an exemplary factor analysis process,according to embodiments of this disclosure.

FIG. 9 illustrates a graphical representation of an exemplary priorityvector, according to embodiments of this disclosure.

FIG. 10 illustrates an exemplary user interface, according toembodiments of this disclosure.

FIG. 11 illustrates a flowchart of an exemplary competitor assessmentprocess, according to embodiments of this disclosure.

FIG. 12 illustrates a flowchart of an exemplary competitor assessmentprocess, according to embodiments of this disclosure.

FIGS. 13A-13B illustrate exemplary data, according to embodiments ofthis disclosure.

FIG. 14 illustrates exemplary data, according to embodiments of thisdisclosure.

FIG. 15 illustrates an exemplary file, according to embodiments of thisdisclosure.

FIG. 16 illustrates a flowchart of an exemplary competitor assessmentprocess, according to embodiments of this disclosure.

FIGS. 17A-17C illustrate exemplary data, according to embodiments ofthis disclosure.

FIG. 18 illustrates a flowchart of an exemplary competitor assessmentprocess, according to embodiments of this disclosure.

FIG. 19 illustrates exemplary data, according to embodiments of thisdisclosure.

FIG. 20 illustrates exemplary data, according to embodiments of thisdisclosure.

FIG. 21 illustrates a flowchart for determining an updated performancescore of the target business, according to embodiments of thisdisclosure.

FIG. 22 illustrates a flowchart for determining an updated performancescore of the target business, according to embodiments of thisdisclosure.

FIG. 23 illustrates an exemplary data, according to embodiments of thisdisclosure.

FIG. 24 illustrates an exemplary data, according to embodiments of thisdisclosure.

FIG. 25 illustrates a flowchart for determining the occurrences ofperformance drivers in the capability process data, according toembodiments of this disclosure.

FIG. 26 illustrates an exemplary data, according to embodiments of thisdisclosure.

FIG. 27 illustrates an exemplary data, according to embodiments of thisdisclosure.

FIG. 28 illustrates an exemplary data, according to embodiments of thisdisclosure.

FIG. 29 illustrates a flowchart for measuring a performance of thetarget business, according to embodiments of this disclosure.

FIG. 30 illustrates a flowchart for determining an updated performancescore of the target business, according to embodiments of thisdisclosure.

FIG. 31 illustrates an exemplary data, according to embodiments of thisdisclosure.

FIG. 32 illustrates an exemplary data, according to embodiments of thisdisclosure.

FIG. 33 illustrates an exemplary KPI, according to embodiments of thisdisclosure.

FIG. 34 illustrates an exemplary KPI, according to embodiments of thisdisclosure.

FIG. 35 illustrates an exemplary recommendation file, according toembodiments of this disclosure.

FIG. 36 illustrates an exemplary user interface, according toembodiments of this disclosure.

FIG. 37 illustrates a flowchart of an exemplary design thinking process,according to embodiments of this disclosure.

FIG. 38 illustrates a flowchart of an exemplary design thinking process,according to embodiments of this disclosure.

FIG. 39 illustrates an exemplary user interface, according toembodiments of this disclosure.

FIG. 40 illustrates a flowchart of an exemplary design thinking process,according to embodiments of this disclosure.

FIG. 41 illustrates an exemplary user interface, according toembodiments of this disclosure.

FIG. 42 illustrates an exemplary user interface, according toembodiments of this disclosure.

FIG. 43 illustrates an exemplary map, according to embodiments of thisdisclosure.

FIG. 44 illustrates an exemplary map, according to embodiments of thisdisclosure.

FIG. 45 illustrates an exemplary map, according to embodiments of thisdisclosure.

FIGS. 46A-46C illustrate an exemplary user interface, according toembodiments of this disclosure.

FIG. 47 illustrates a flowchart of an exemplary design thinking process,according to embodiments of this disclosure.

FIG. 48 illustrates a flowchart of an exemplary design thinking process,according to embodiments of this disclosure.

FIG. 49 illustrates an exemplary collaboration environment, according toembodiments of this disclosure.

FIG. 50 illustrates an exemplary collaboration environment, according toembodiments of this disclosure.

FIG. 51 illustrates an exemplary collaboration environment, according toembodiments of this disclosure.

FIG. 52 illustrates a flowchart of an exemplary design thinking process,according to embodiments of this disclosure.

FIG. 53 illustrates an exemplary prototyping environment, according toembodiments of this disclosure.

FIG. 54 illustrates a flowchart of an exemplary design thinking process,according to embodiments of this disclosure.

FIG. 55 illustrates an exemplary map, according to embodiments of thisdisclosure.

FIG. 56 illustrates a flowchart of an exemplary design thinking process,according to embodiments of this disclosure.

FIG. 57 illustrates a flowchart of an exemplary design thinking process,according to embodiments of this disclosure.

FIG. 58 illustrates a flowchart of an exemplary design thinking process,according to embodiments of this disclosure.

FIG. 59 illustrates a flowchart of an exemplary design thinking process,according to embodiments of this disclosure.

DETAILED DESCRIPTION

In the following description of examples, reference is made to theaccompanying drawings which form a part hereof, and in which it is shownby way of illustration specific examples that can be practiced. It is tobe understood that other examples can be used and structural changes canbe made without departing from the scope of the disclosed examples.

Embodiments of this disclosure relate to a system for improving multipleareas, such as strategy, operations, risk management, and regulationcompliance, of a target business. FIG. 1 illustrates a block diagram ofa system 100, according to embodiments of this disclosure. The system100 may provide different functionalities to achieve these improvements.For example, the system 100 may include functionality to provide one ormore initial scores of a target business, assess one or more processcapabilities of the target business, set one or more KPIs, set one ormore business goals, provide a collaboration platform for stakeholdersin the target business, and provide a roadmap for achieving one or morebusiness goals. These functionalities may be performed by one or more ofthe processes discussed below.

The functionality of the system 100 may be grouped into differenthigh-level functions, such as assessment 1, process decomposition 2, KPIsetting 3, design thinking 4, and roadmap 5. The high-level functionscan be presented to a user using a user interface (UI), for example.

Exemplary Process for Providing One or More Initial Scores

The system 100 may be configured to provide an assessment of businesspractices and processes for a target business. In some embodiments, theassessment may be based on a comparison between the target businessagainst a benchmark competitor. The assessment may be used by a user,such as a business leader of the target business, for example. The usermay use the assessment information to determine areas of growth andimprovement for the target business.

As discussed in more detail below, the system 100 may perform one ormore of: determining an initial performance score of the targetbusiness, identifying a benchmark competitor (e.g., the industryleader), and determining a benchmark performance score of the benchmarkcompetitor.

FIG. 2 illustrates a block diagram of a portion of an exemplary system100, according to embodiments of this disclosure. The system 100 mayinclude a website 201, an application programming interface (API) 203, afirst database 205, a second database 207, a network 213, first data215, and second data 209.

The website 201 may an interface between a user 211 and the system 100,as shown in the figure. The website 201 may include a user interface(UI) that may be accessed by various employees (e.g., users 211) of thetarget business, for example. The website 201 may be integrated with theapplication programming interface (API) 203. The API 203 may serve as anintermediary between the website 201 and one or more databases, such asthe first database 205.

The first database 205 may be any type of database, such as a clouddatabase (e.g., Azure SQL, Amazon Web Services, etc.). In someembodiments, the first database 205 may be in communication with aremote server that maintains a second database 207.

The second database 207 may store data received from one or moresources, such as from a user 211 accessing the website 201, the internet213, first data 215, etc. The first data 215 may be from a third partysource, for example. The second database 207 may store second data 209,which may be data determined by the system 100. In some embodiments,second data 209 may include one or more scores, such as an initialperformance score of the target business and a benchmark performancescore of a benchmark competitor, or a combination thereof.

As discussed above, the system 100 may determine one or more scoresincluded in second data 209. An exemplary score may be an initialperformance score of a target business. The initial performance score ofthe target business may be based on one or more target business domainscores, where each target business domain score may be representative ofthe target business' performance in the respective domain. For example,each of the target business domain scores may be evaluated against atarget business domain weight to determine the target business domainscore.

FIG. 3 illustrates a flow chart of an exemplary operation 300 of asystem 100, according to embodiments of this disclosure. Although theprocess 300 is illustrated as including the described elements, it isunderstood that different order of elements, additional elements, orfewer elements may be included without departing from the scope of thedisclosure.

The system 100 can perform a target business assessment process 310 anda benchmark competitor assessment process 320. The target businessassessment process 310 can determine the initial performance score of atarget business. The benchmark competitor assessment process 320 canidentify a benchmark competitor and determine a benchmark performancescore of the benchmark competitor. Both processes are discussed in moredetail in turn below.

FIG. 4 illustrates a flow chart of an exemplary target businessassessment process 310 for determining an initial performance score of atarget business, according to embodiments of this disclosure. Althoughthe process 310 is illustrated as including the described elements, itis understood that different order of elements, additional elements, orfewer elements may be included without departing from the scope of thedisclosure.

Process 310 may begin with receiving information from a self-assessment,in step 311. For example, a user (e.g., a business leader) within thetarget business may access the system 100. A business leader may includea chief executive officer (CEO), chief operations officer (COO), orother highly ranked individual employed by the target business. Thesystem 100 can give the user login credentials or can prompt the user tocreate a log-in when accessing a website 201 via the internet. On thewebsite 201, the user may select the function related to assessment 1.

In some embodiments, the system 100 may provide the user withintroductory questions. The system 100 may receive introductoryinformation regarding the target business as the user answers theintroductory questions. Exemplary introductory information may includeto, but are not limited to, the type of industry, the number ofemployees, location, revenue, business volume, etc. In some embodiments,the system 100 may build a target business profile using theintroductory questions and/or introductory answers.

Additionally or alternatively, in step 313, the system 100 can provide aself-assessment questionnaire to the user. The questionnaire may includequestions about the target business and the target business'performance. In some embodiments, the system 100 can dynamically selectthe questions in the questionnaire as the user completes it. Forexample, the system 100 can select questions based on the completedanswers to the introductory questions (provided in step 311) or to thequestionnaire (provided in step 313). The questionnaire may include aseries of questions related to the culture, technology, knowledgecuration, data strategy, compliance, partner enablement, performancemeasurement, business processes, and other areas of business strategy.

In step 313, the questions may be categorized into different types ofquestions, such as fact-gathering questions and domain-weighingquestions. In some examples, a user may not be able to distinguishbetween a fact-gathering question and a domain-weighing question. Forexample, the fact-gathering questions and domain-weighing questions maybe phrased in a similar manner and formatted to receive answers in asimilar manner. The system 100 may associate each question with theappropriate category and save this categorization into the seconddatabase 207.

The fact-gathering questions may be asked to determine specific factsand attributes of the target business. In some embodiments, thefact-gathering questions may be specific to one or more domains. Adomain may refer to an area of a business that can impact a score of thebusiness. The domains may be categorized into industry domains (e.g.,domains specific to a particular industry) and common domains (e.g.,domains common to many industries). An example of a common domainfact-gathering question may be “to what extent are appropriate securitycontrols in place to ensure the integrity of explicit knowledgesources?” The domain-weighing questions may be asked to determine theimportance of one or more domains for the target business. An example ofa common domain, domain-weighing question may be “to what extent do youconsider your technology upgrade will impact your top and bottom linefinancials?”

In some embodiments, the system 100 may allow the user to provideanswers to the fact-gathering and domain-weighing questions by selectinga numerical value (e.g., on a scale of 1-5, on a scale of 1-10, on ascale of 1-100, etc.). For example, in response to the fact-gatheringquestion, the answer choices may be selected from a scale of 1-5, where1 corresponds to “no procedures in place” and 5 corresponds to “contentretirement and review rules are consistently applied to information andknowledge; enabling technologies are used to support efforts to maintainknowledge currency.” As another example, in response to the commondomain, domain-weighing question, the answer choices may be selectedfrom a scale of 1-5, where 1 corresponds to “minimal impact: most of ourprocesses are easily performed manually with no significant productivityloss” and 5 corresponds to “significant impact: our organization maysave a lot on operational losses if the technology stack is digitizedwith automation.” In some examples, the system 100 may allow the user toenter in a written response, and natural language processing (NLP) maybe used to determine a corresponding numerical value.

In step 315, the system 100 may determine one or more target businessdomain weights based on answers to the domain-weighing questions. Insome embodiments, the system 100 may use target business domain weightsto determine the relative importance of the one or more domains to thetarget business. For example, answers to the domain-weighing questionsmay indicate which domains have a greater impact on the initialperformance score of the target business.

In step 317, the system 100 may determine the initial performance scoreof the target business. The initial performance score of the targetbusiness can be based on answers to the fact-gathering questions. Insome embodiments, the fact-gathering questions may be specific to one ormore domains for the target business. The target business domain weightsmay be applied to the answers from the fact-gathering questions in thecorresponding domain of the target business to generate the initialperformance score of the target business.

Another exemplary score included in second data 209 may be a benchmarkperformance score of a benchmark competitor. The benchmark performancescore of a benchmark competitor may be based on third-party data, e.g.,a first data 215, related to the benchmark competitor. The system 100can process the third-party data to determine the benchmark performancescore of the benchmark competitor.

FIG. 5 illustrates a flowchart of an exemplary benchmark competitorassessment process 320, according to embodiments of this disclosure. Thebenchmark competitor assessment process 320 may comprise identifying abenchmark competitor (e.g., industry leader) (sub-process 350) anddetermining a benchmark score (e.g., a benchmark performance score(sub-process 600) and one or more benchmark competitor domain scores(sub-process 1100)) of the benchmark competitor. Although process 320 isillustrated as including the described elements, it is understood thatdifferent order of elements, additional elements, or fewer elements maybe included without departing from the scope of the disclosure.

Process 320 may begin with step 321 where the system 100 may determineinformation (e.g., including data) about one or more competitors in thesame industry as the target business. In some embodiments, the industrymay be one identified by the target company based on answers receivedfrom introductory questions (in step 311) and/or from a self-assessmentquestionnaire (step 313). In some examples, the system 100 may determinea business is a competitor based on one or more attributes (e.g., size,demography, location, etc.) similar to the target business. In someembodiments, the system 100 can include information about thecompetitor(s) in first data 215. First data 215 may be data from a thirdparty source. In some examples, the system 100 can obtain first data 215from a network 213.

In step 323, the system 100 may perform pre-processing on the first data215. Pre-processing may include standardizing and cleaning the firstdata 215, so that the system 100 can further process and analyze thefirst data 215. The system 100 can perform a factor analysis process todetermine factor analysis weights (step 325) and multiple-criteriadecision-analysis and analytic hierarchy process (MCDA-AHP) to determineMCDA-AHP weights (step 327). In some embodiments, the factor analysisand MCDA-AHP analysis may be performed in parallel. The factor analysisweights and the MCDA-AHP weights may be applied to the first data 215.

In step 329, the system 100 can use the factor analysis weights and theMCDA-AHP weights to determine mean adaptive weights. The system 100 canthen apply the mean adaptive weights to the pre-processed first data 215to determine performance scores of each of the competitors, in step 331.

The system 100 may also determine the attributes of the target business,in step 333. For example, the target business' attributes may be basedon the answers to the introductory questions received in step 311.

In step 335, the system 100 can apply heuristic rules to the targetbusiness' attributes to ensure the selected benchmark competitor hasattributes similar to the target business. The competitor with thehighest performance score with attributes similar to the target businessmay be identified as the benchmark competitor (step 337).

In some embodiments, the system 100 can determine one or more benchmarkcompetitor domain scores. The benchmark competitor domain scores mayrepresent the benchmark competitor's performance in one or more domains.Once the benchmark competitor is identified, the system 100 can acquiredata specific to the benchmark competitor, in step 339. The system 100can process the acquired data to determine one or more benchmarkcompetitor domain scores, in step 341.

Once the system 100 has determined the scores (e.g., initial performancescore of the target business, target business domain scores, benchmarkperformance score of the benchmark competitor, and benchmark competitordomain scores), the system 100 can provide a graphical representation ofthe results to the user via the website 201. The graphicalrepresentation may be in the form of a spider graph, comparative bargraph, or other suitable graphical representation. The graphicalrepresentation may allow the user (e.g., a business leader at the targetbusiness) to view and understand their business' performance and be ableto compare the performance of the target business against the benchmarkcompetitor.

Exemplary Process for Identifying a Benchmark Competitor

FIG. 6 illustrates a detailed flowchart of an exemplary sub-process 600for determining a performance score of one or more competitors,according to embodiments of this disclosure. Although the process 600 isillustrated as including the described elements, it is understood thatdifferent order of elements, additional elements, or fewer elements maybe included without departing from the scope of the disclosure.

In some embodiments, the system 100 may not have direct access toinformation from one or more competitors to determine the performancescores of the competitors. In such instances, the system 100 may acquirethe information. In step 401, the system 100 can acquire data (e.g.,first data 215) about one or more competitors in the same industry asthe target business from third party sources. The industry may bedetermined based on answers to the introductory questions, provided bythe user in step 311. In some embodiments, the first data 215 may beacquired from third party companies that monitor and aggregateinformation, web scraping, and other data source channels. The firstdata 215 may be collected, maintained, and stored in the second database207.

The first data 215 may include information specific to the one or morecompetitors. Information may include, but is not limited to, size (e.g.,number of employees), revenue, business volume, demography, and thelike. In some embodiments, the first data 215 may also include multipleperformance-related variables of the competitor, where aperformance-related variable may refer to an operational area ormeasurable output of the competitor's business that may impact theoverall performance of the competitor's business. In some embodiments,the variables of the first data 215 may depend on available datametrics. In some embodiments, the variables may be based on theinformation provided by the user during the target business assessmentprocess. In some embodiments, the system 100 can search the seconddatabase 207 for information regarding competitors in the same industryas the target business before requesting and collecting the first data215 from external sources (e.g., third party sources).

In step 403, the system 100 can pre-process (e.g., reformat and clean)the first data 215. In some instances, the first data 215 may not bestandardized and may include gaps in the data and/or outliers that canimpact the accuracy of processing the first data 215. Embodiments of thedisclosure include the system 100 pre-processing the first data 215 tomanage outliners, handle missing data, and standardize the first data215 to be on the same scale (e.g., 1-100, 1-10, etc.), fix structuralerrors, or a combination thereof. Structural errors may include, but arenot limited to, skipped delimiter, duplicate delimiter in a given row,absence of uniqueness in the primary row ID, typos, mislabeled data,same attributes with different name (e.g., “America” and “America” maybe handled differently without pre-processing), mixed types (e.g., mixednumerals and strings). In some examples, the first data 215 may bequantitative data. The system 100 can pre-process the quantitative datato standardize each of the variables by the one or more competitors. Insome examples, the first data 215 may be qualitative data. In suchexamples, NLP may be used to quantify and standardize the first data215.

An exemplary competitor performance data 700 that has been pre-processedin step 403 is shown in FIG. 7 . In the example shown in the figure, thetarget business may be a university. The system 100 may collect data fora number of competitors 710. The data may include general information715 (e.g., name, country, etc.) as well as data related to variablesthat impact the competitor-universities' business performance 720 (e.g.,teaching, research, citations, industry income, international outlook,etc.).

Although FIG. 7 shows eleven competitors 710 and six variables 720,embodiments of the disclosure may include any number of competitors andany number of variables. For example, the pre-processed data may includeover 100 variables 720 related to the competitors 710. In someembodiments, the performance data can include an “overall score” thatdetermined by the third party source. In some embodiments, the “overallscore” 720, indicated in the performance data 700 may be different thanthe competitor performance score determined by the system 100.

Referring back to FIG. 6 , once the system 100 pre-processes the firstdata 215, the system 100 may analyze the data, in step 405. Step 405 maybe used to identify the most salient variables that impact theperformance scores of the competitors. Identifying the most salientvariables may allow the system 100 to reduce noise introduced byunimportant variables, resulting in more accurately interpreted data.

The data analysis (in step 405) may include performing a factor analysisprocess 800 and performing a MCDA-AHP analysis process (steps 419-421).In some embodiments the factor analysis and the MCDA-AHP analysis may beperformed in parallel. The factor analysis process 800 may include steps407-417 and is discussed in more detail below. The factor analysisprocess 800 may include computing a correlation matrix (step 407),determining correlation coefficients (step 409), identifying extractionfactors (step 411), determining relevant factors (step 413), maximizingvariance shared between factors to minimize loading on each factor (step415), and determining one or more factor analysis weights (step 417).Determining correlation coefficients will be discussed in more detailbelow.

For the factor analysis process 800, the extraction factors identifiedin step 411 can be determined by determining the eigenvalues of thefactors, creating a screen plot, and the like. In step 413, the relevantfactors determined can be based on the eigenvalues. For example,eigenvalues of greater than or equal to a correlation threshold may beconsidered relevant. In step 415, the maximization of variance can beperformed using a factor rotation (e.g., a varimax rotation).

The MCDA-AHP analysis process may include applying a pairwise preferencecomparison between different variables (step 419) and normalizing thepairwise preference comparison to determine MCDA-AHP analysis weights(step 421). The pairwise preference comparison in step 419 may includeperforming a comparison between the variables. In step 421, the MCDA-ADPanalysis weights can be determined with respect to each variable 720 inthe first data 215. The MCDA-ADP analysis weights may be indicative ofthe relative impact that each variable 720 has on the performance scoresof the competitors.

The performance scores of the competitors may be determined usingadaptive weights. In step 423, the system 100 may determine adaptiveweights based on the factor analysis weights and the MCDA-AHP analysisweights. In step 425, the system 100 may apply the adaptive weights tothe pre-processed first data 215 to determine the performance scores ofthe competitors.

FIG. 8 illustrates a flowchart of an exemplary factor analysis process800, according to embodiments of this disclosure. Although the process800 is illustrated as including the described elements, it is understoodthat different order of elements, additional elements, or fewer elementsmay be included without departing from the scope of the disclosure.

The factor analysis process 800 may be performed to filter outirrelevant variables and reduce noise introduced by irrelevantvariables. For example, a clean competitor performance data 700 caninclude a large number of variables 720. In some examples, the number ofvariables 720 may be large (e.g., exceed fifty or one hundred). Some ofthe variables 720 in the competitor performance data 700 may be morerelevant than others to the performance score of a competitor. To reducenoise introduced by irrelevant variables, the system 100 can perform afactor analysis process 800 to identify relevant variables 720 andassign relative factor analysis weights to the variables 720. Forexample, variables 720 with a greater impact on the performance score ofa competitor can be associated with a higher factor analysis weight.

In step 801, the system 100 can determine a correlation matrix based onthe variables 720 included in the cleaned first data 215. Thecorrelation matrix may be indicative of the variance between thevariables 720. For example, based on the data provided in Table 1(below), the correlation matrix can compare the teaching, research,citations, industry income, and international outlook variables 720 forthe universities. The correlation matrix may be used to determine whichvariables 720 are related. For example, a correlation coefficient of 1may indicate that the variables 720 are highly correlated. A correlationcoefficient of 0 may indicate that the variables 720 are not highlycorrelated. The system 100 can determine the relevant variables 720 bysetting a factor threshold for the correlation coefficient. Variables720 with a correlation coefficient greater than or equal to the factorthreshold may be used in the factor analysis process 800. In someexamples, the factor threshold may be between 0.25-0.5. In someembodiments, the Bartlett Test of Sphericity and the Kaiser-Meyer-Olkin(KMO) measure of sampling adequacy may be used to test the accuracy ofthe correlation matrix.

In step 802, the system 100 may identify factors underlying thevariables 720. In some embodiments, the factors can be a numericalrepresentation of the relationship between the variables 720. Estimatesof the factors may be obtained using, for example, eigenvalues, a screeplot, and the like. Table 1 shows an example of five eigenvaluesdetermined from five variables 720 (teaching, research, citations,industry income, and international outlook) for the respectivecompetitor performance data 700 (shown in FIG. 7 ). A higher eigenvalueindicates that the factor is more predictive of the relationship betweenthe variables 720. Factors with an eigenvalue above a pre-determinedcorrelation threshold may be selected and used in subsequent steps ofthe factor analysis process 800. In some examples, the correlationthreshold may be greater than or equal to one, greater than or equal to1.0-0.75, etc. In the example shown in Table 1, the pre-determinedcorrelation threshold can be greater than or equal to 0.9. Factors 1 and2 are above the predetermined correlation threshold and are used in thenext step 803 of the factor analysis process 800.

TABLE 1 Factor Eigenvalue 1 3.09214 2 0.985964 3 0.555588 4 0.371948 50.077287

In step 803, the system 100 may perform a factor rotation on theidentified factors. The factor rotation can enable the system 100 to bemore readily and meaningfully interpret the factors. For example, thesystem can apply a Varimax rotation to the factors. The Varimax rotationmay apply orthogonal rotations, which results in maximizing the squareof the variances. The rotation can minimize the number of variables 720that have high loadings on a factor, thereby enhancing theinterpretability of the factors. In some examples, the system 100 caninterpret a factor by examining the largest values linking the factor tothe measured variables 720 in the rotated factors.

TABLE 2 Variables Factor 1 Factor 2 Teaching 0.804104 0.377571 Research0.879805 0.488158 Citations 0.264200 0.803559 Industry Income 0.5223930.072550 International Outlook 0.194364 0.668968

Table 2 (above) provides the values from a factor rotation of factors 1and 2. Factors 1 and 2 in Table 2 may correspond to factors 1 and 2 ofTable 1, and the variables 720 in Table 2 may correspond to the fivevariables 720 shown in FIG. 7 . As seen in Table 2, factor 1 is moststrongly linked with the teaching and research variables, while factor 2is most strongly linked with the citations variable and somewhat linkedto the international outlook variable.

The system 100 can also determine the variance of the factors. Thevariance corresponds to the amount of variation between the variables720 of each factor. Variables 720 that are highly correlated may have ahigher variance. For example, as shown in Table 3 (below), factor 1 hasa higher variance than factor 2. The system 100 may also determine theproportional variances and the cumulative variances of factors 1 and 2,as shown in Table 3.

TABLE 3 Factor 1 Factor 2 Variance (SS loadings) 1.801114 1.479347Proportional Variance 0.360223 0.295869 Cumulative Variance 0.3602230.656092

In step 804, the system can transform and apply the variance to thefactors to determine the factor analysis weights. The variance shown inTable 3 can be expressed in terms of deviations from the mean. In someinstances, the variance cannot be directly applied to the variables offactor 1 and factor 2. The system 100 can apply a transformationfunction to the variance of each variable 720 to normalize the values. Anormalized variance value may represent a number on a scale having aminimum value of zero. In some embodiments, the factor transformationapplied to the variances can be represented as:

$\begin{matrix}{{f\left( F_{i} \right)} = \left\{ \begin{matrix}{{1 + {\frac{k - 1}{2}e^{F_{i}}\ si\ F_{i}}} < 0} \\{k + {\frac{k - 1}{2}e^{- F_{i}}\mspace{14mu}{in}\mspace{14mu}{another}\mspace{14mu}{way}}}\end{matrix} \right.} & (1)\end{matrix}$

The system 100 can apply representation (1) to perform the factortransformation, where k is a variance and F is a coefficient for avariable 720 of a factor. Table 4 shows the factor transformationapplied to factor 1 and factor 2 in the university example.

TABLE 4 Weighted Variables Factor 1 f(F₁) Factor 2 f(F₂) Sum Teaching0.804104 99.98 0.377571 65.81 55.08 Research 0.879805 79.21 0.48815869.37 48.63 Citations 0.264200 61.81 0.803559 77.75 44.80 IndustryIncome 0.522393 70.57 0.072550 53.74 40.99 International 0.194364 59.060.668968 74.41 42.84 Outlook

The system 100 can apply the factor transformation to determine aweighted variable value for each of the relevant variables 720.Referring to Table 4, the system 100 can determine a weighted variablevalue or weighted sum by taking the sum of each factor transformation(e.g., f(F₁), f(F₂), etc.) and multiplying by the correspondingproportional variance for each variable 720.

$\begin{matrix}{I_{AFM} = {\sum\limits_{i = 1}^{p}{{f\left( F_{i} \right)} \times \frac{{Variance}\mspace{14mu}{Explained}\mspace{14mu}{per}\mspace{14mu} F_{i}}{{Total}\mspace{14mu}{Variance}}}}} & (2)\end{matrix}$

For example, to determine the weighted sum for the teaching variable720, the factor transformation for factor 1 f(F₁) can be multiplied byits corresponding factor 1 proportional variance (0.36) to result in afirst value. The factor transformation for factor 2 f(F₂) can bemultiplied by its corresponding factor 2 proportional variance (0.29) toresult in a second value. The first value can be added to the secondvalue to generate the following weighted sum:Weighted Sum=0.36*0.804104+0.29*0.377571=55.08.The system 100 can determine the factor analysis weights based on theweighted sum.

In some embodiments, the system 100 can determine the factor analysisweights by dividing the weighted sum of a variable 720 by the totalweighted sum of the variables 720 multiplied one hundred. Table 5provides the factor analysis weights determined for each of thevariables 720.

TABLE 5 Variables Factor Analysis Weights Teaching 24% Research 21%Citations 19% Industry Income 18% International Outlook 18%

In some embodiments, a MCDA-AHP analysis may be performed to determine asecond set of weights (i.e., MCDA-AHP analysis weights) using MCDAintegrated with AHP. MCDA can be used to evaluate multiple interactiveand, in some cases, conflicting variables in decision making. Forexample, alternative variables in decision making may include cost andquality. In some situations, the cost may conflict with the quality. Forexample, a business may aim to lower costs while maintaining highquality results, but maintaining high quality may result in highercosts. MCDA provides a framework that can be used to partition the issueinto smaller, manageable blocks. The smaller blocks can be analyzed, andthe analysis can be integrated to provide a solution. AHP can be used toquantify the MCDA-AHP analysis weights of the variables 720 and itsimpact on the performance score of a competitor. The framework providedby MCDA and AHP provides a basis for the MCDA-AHP analysis process.

Referring to back to FIG. 6 , the MCDA-AHP analysis process includesperforming a pairwise preference comparison between the variables (instep 419) and determining MCDA-AHP analysis weights based on thepairwise preference comparison (in step 421). Variables can beidentified in a hierarchical manner (e.g., ranked). Ranking thevariables provides a way to filter out variables that may not stronglyimpact the performance score of a competitor. As explained above, theremay be a large number of variables 720 included in the competitorperformance data 700, and reducing the number of variables 720 (e.g., tothe most meaningful variables 720) may enhance the accuracy of theanalysis.

In some examples, the hierarchy may be determined based on answersprovided by the user during the self-assessment step (e.g., step 313).As discussed above, the self-assessment can include questionnaire havinga number of fact-gathering questions and domain-weighing questions.While the fact-gathering questions can be used to determine specificfacts and attributes of the target business, the domain-weighingquestions can be used to determine the importance of various domains andvariables. In this manner, the MCDA-AHP analysis weights arecustomizable to a competitor. This customization allows anapples-to-apples comparison of variables 720 considered to be ofimportance to the target business. In some embodiments, ranking thevariables may involve subject matter expert (SME) opinions and/or basedon responses to the fact gathering questions from the target businessassessment 310. In some examples, the initial variables in the MCDA-AHPanalysis may be the variables with a correlation coefficient above thecorrelation threshold as discussed in step 802.

The system 100 can evaluate the relevant variables 720 (identified instep 409) using a pairwise preference comparison approach. The pairwisepreference comparison approach compares values of relative importancebetween each pair of variables 720 and stores this comparison in acomparison matrix. Table 6 (below) shows an exemplary pairwisepreference comparison matrix after applying a pairwise preferencecomparison to the competitor performance data 700 (of FIG. 7 ).

TABLE 6 Inter- Industry national Teaching Research Citations IncomeOutlook Teaching 1.000000 1.000000 1.000000 9.000000 4.00 Research1.000000 1.000000 1.000000 9.000000 4.00 Citations 1.000000 1.0000001.000000 9.000000 4.00 Industry 0.111111 0.111111 0.111111 1.000000 0.33Income International 0.250000 0.250000 0.250000 3.030303 1.00 Outlook

Once the pairwise preference comparison matrix is determined, thepairwise preference comparison can be normalized. Normalization may beperformed to scale the pairwise preference comparison matrix values to ascale of 0 to 1. Relationship (3) can be applied to the values in thecomparison matrix to produce a normalized pairwise preference comparisonmatrix.

$\begin{matrix}{{{A_{normalized}\mspace{14mu}{are}\mspace{14mu} a_{ij}^{\prime}} = {{\frac{a_{ij}}{\sum_{i = 1}^{n}a_{ij}}\mspace{14mu}{for}\mspace{14mu} j} = 1}},2} & (3)\end{matrix}$Table 7 illustrates a normalized pairwise preference comparison matrixbased on the values of the comparison matrix provided in Table 6.

TABLE 7 Industry International Teaching Research Citations IncomeOutlook Rsum Vector Teaching 0.297521 0.297521 0.297521 0.2900390.300075 1.482676 0.296535 Research 0.297521 0.297521 0.297521 0.2900390.300075 1.482676 0.296535 Citations 0.297521 0.297521 0.297521 0.2900390.300075 1.482676 0.296535 Industry 0.033058 0.033058 0.033058 0.0322270.024756 0.156156 0.031231 Income International 0.074380 0.0743800.074380 0.097656 0.075019 0.395816 0.079163 Outlook

Pairwise weights for each of the variables 720 may be determined basedon the normalized pairwise preference comparison matrix. Based on thenormalized values, the system 100 can determine a priority vector. Thepriority vector may correspond to the MCDA-AHP analysis weights. Thepriority vector can be determined from the normalized pairwisepreference comparison matrix using relationship (4). The values of thepriority vector for Table 7 is shown in the Vector column.

FIG. 9 illustrates a graphical representation of an exemplary priorityvector, illustrating the relative magnitude of the pairwise weights ofeach of the factors.

$\begin{matrix}{{v_{i} = {{\frac{\sum_{j = 1}^{n}a_{ij}^{\prime}}{n}\mspace{14mu}{for}\mspace{14mu} i} = 1}},2,\ldots\mspace{14mu},n} & (4)\end{matrix}$

Referring back to FIG. 6 , once the factor analysis weights and theMCDA-AHP analysis weights have been determined using the factor analysisprocess 800 (steps 407-417) and the MCDA-AHP analysis process (steps419-421), respectively, the system 100 can determine the adaptiveweights, in step 423. In step 425, the adaptive weights may be appliedto the variables 720 for each competitor to determine the respectiveperformance score. In some examples, the adaptive weights can bedetermined by taking the mean of the factor analysis weights and theMCDA-AHP analysis weights.

Table 8 shows exemplary factor analysis weights, MCDA-AHP analysisweights, and adaptive weights for variables 720 of the universityexample (discussed above). As shown in the table, each adaptive weightmay be equal to the average of a corresponding factor analysis weightand a corresponding MCDA-AHP analysis weight.

TABLE 8 MCDA-AHP Factor Analysis Analysis Adaptive Variables WeightsWeights Weights Teaching 24%   30% 0.27 Research 21% 29.50% 0.25Citations 19% 29.50% 0.24 Industry Income 18%    3% 0.11 International18%    8% 0.13 Outlook

In some examples, the factor analysis weights may be considered moreimportant than the MCDA-AHP analysis weights, and such importance may bereflected by using a scaling factor. For example, the factor analysisweights may be multiplied by a first scaling factor (e.g., 1-5). In someembodiments, the MCDA-AHP analysis weights may be also multiplied by a(second) scaling factor (e.g., 1-5). The first scaling factor, secondscaling factor, or both may be used before taking the average. Thefactor analysis weights or the MCDA-AHP analysis weights may be givenmore importance based on the scaling factors.

In some examples, additional analyses can be used to determine any ofthe above discussed weights for the variables 720. In such examples, theaverage of the weights across all the analyses may be taken.

In step 425, the performance score for each competitor can be determinedusing the adaptive weights determined in step 423. The performance scorecan be determined using relationship (5), for example.

$\begin{matrix}{\overset{¯}{x} = {\frac{\sum_{i = 1}^{n}{w_{i} \cdot x_{i}}}{\sum_{i = 1}^{n}w_{i}} = \frac{{w_{1}x_{1}} + {w_{2}x_{2}} + \ldots + {w_{n}x_{n}}}{w_{1} + w_{2} + \ldots + w_{n}}}} & (5)\end{matrix}$

The system 100 may apply a weighted average x, which may be equal to thesum of the product of the adaptive weight for a particular variablew_(i) times the corresponding value x_(i) for each variable 720 dividedby the sum of the weights. The weighted average x may be used todetermine the performance score of a competitor. Table 9 shows theperformance scores for competitors 710 using the competitor performancedata 700.

TABLE 9 Inter- Bench- Teach- Cita- Industry national mark ing Researchtions Income Outlook Score U of Oxford 90.5 99.6 98.4 65.5 96.4 4.63 CalIT 92.1 97.2 97.9 88 82.5 4.65 U of 91.4 98.7 95.8 59.3 95 4.56Cambridge Stanford U 92.8 96.4 99.9 66.2 79.5 4.54 MIT 90.5 92.4 99.586.9 89 4.63 Princeton 90.3 96.3 98.8 58.6 81.1 4.5  Harvard 89.2 98.699.1 47.3 76.3 4.33 Yale 92 94.8 97.3 52.4 68.7 3.99 U of Chicago 89.191.4 96.7 52.7 76 4.31 Imperial 84.5 87.6 97 69.9 97.1 4.54 CollegeLondon U of Penn 87.5 90.4 98.2 74 65 4.19

The performance scores for the competitors 710 may be used to identifythe benchmark competitor. In some embodiments, the system 100 may selectthe competitor with the highest performance score as the benchmarkcompetitor. For example, based on the determined performance scoresshown in Table 9, California Institute of Technology may be selected asthe benchmark competitor.

Referring back to FIG. 5 , in some examples, the system 100 maydetermine attributes of a target business (step 333). As discussedabove, the attributes of the target business can be identified usinginformation from the introductory questions and/or self-assessment steps(step 311 and/or step 313). The competitors that do not share attributessimilar to the target business may be eliminated from a selection pool.

Using the university example, attributes such as size and location ofthe university may be used to narrow the selection pool. A skilledartisan will understand that a number of other attributes may also beused. In some embodiments, the system 100 can apply heuristic rules(step 335). For example, a condition such as “all the attributes shouldmatch” may be applied. If no data match is found, the attribute matchcan be relaxed on one attribute at a time until there is a match. Insome embodiments, attributes determined as being less relevant tobusiness performance may be relaxed before others. For example, in someindustries, the location attribute may be determined to be less relevantto performance. In this manner, the system 100 can maximize the numberof relevant attributes between the target organization and the benchmarkcompetitor. Narrowing the selection pool based on the targetuniversity's attributes may provide a stronger comparison thanidentifying the benchmark competitor based on the highest performancescore. In this manner, the identified benchmark competitor may becompared to the target business using an apples-to-apples comparison.

In some examples, the selection pool may be determined at step 321 wheredata about the competitor is acquired. In such examples, the competitorsidentified in a competitor performance data 700 may be representative ofcompetitors with similar attributes to the target business.

Embodiments of the disclosure may include presenting the identifiedbenchmark competitor via the website 201. FIG. 10 illustrates anexemplary UI 1000 of a website 201, according to embodiments of thisdisclosure. The UI 1000 may display the initial performance score of thetarget business 1001 as well as the benchmark performance score of thebenchmark competitor 1003.

Exemplary Process for Determining Benchmark Competitor Domain Scores

As discussed above, the system 100 can identify a benchmark competitorand the benchmark performance score of a benchmark competitor. Thesystem 100 can also determine one or more benchmark competitor domainscores for one or more domains. As discussed above, a user (e.g., of thetarget business) may answer questions so that the system can determineone or more target business domain scores. The system may not have asimilar direct line of communication with the identified benchmarkcompetitor. For example, the identified benchmark competitor may nothave completed a self-assessment. As a result, the system 100 may needto collect and process data regarding the identified benchmarkcompetitor to determine the benchmark competitor domain scores.

The domains may correspond to areas of a business that can be correlatedto the overall performance of the business. For example, the domains caninclude company culture, technology, facilities, inventory, procurement,compliance, academics, placement, and the like. In some examples,domains may be classified based on research of industry processesprovided by a SME. As discussed above, domains may include industrydomains (e.g., domains specific to a particular industry) and commondomains (e.g., domains common to more than one industry).

FIG. 11 illustrates a detailed flowchart of an exemplary sub-process1100 for determining one or more benchmark competitor domain scores,according to embodiments of this disclosure. Although the process 1100is illustrated as including the described elements, it is understoodthat different order of elements, additional elements, or fewer elementsmay be included without departing from the scope of the disclosure.

In step 1101, the system 100 can acquire data (e.g., first data 215)related to the industry of the target business and the identifiedbenchmark competitor. The first data 215 may be acquired from thirdparty data sources, web scraping, etc., for example. The first data 215may be collected, maintained, and stored in the second database 207. Thefirst data 215 may include quantitative, as well as qualitative data,specific to competitors (including the benchmark competitor determinedin step 337) in the industry. In some examples, the first data 215 mayinclude reviews, complaints, ratings, data providers industry raw data,and the like.

In step 1103, the acquired first data 215 can be processed (e.g.,classified and structured) according to keywords and positive ornegative sentiments. In step 1105, the system can use the processedfirst data 215 to train domain-scoring model(s). The traineddomain-scoring model(s) can be applied and used to determine thebenchmark competitor domain scores based on the first data 215, in step1107.

FIG. 12 illustrates a more detailed flowchart of an exemplary process1103 for processing the first data 215. Although the process 1103 isillustrated as including the described elements, it is understood thatdifferent order of elements, additional elements, or fewer elements maybe included without departing from the scope of the disclosure.

In step 1203, the system can acquire the data (e.g., first data 215)related to competitors. The acquired data may be in the form of anunstructured data file, for example. For example, the first data 215 mayinclude a qualitative review written about the competitor.

FIGS. 13A and 13B illustrate exemplary unstructured data files 1301 and1303, according to embodiments of this disclosure. An unstructured datafile can include qualitative reviews written about a business (e.g., auniversity). The unstructured files 1301 and 1303 may be processed, asdescribed above with respect to FIG. 12 , and converted into astructured data file.

Referring back to FIG. 12 , in step 1205, the system 100 can tokenizethe unstructured data file by sentence. The tokenized files can becombined to form components of a structured data file, in step 1207. Thetokenized components may include reviews. Each of the tokenized filescan form components of the structured data file.

FIG. 14 illustrates an exemplary structured data file 1401, according toembodiments of this disclosure. In this example, the structured data1401 can correspond to the unstructured data file 1301. As shown in FIG.14 , a structured data file 1401 can include one or more of tokenizedcomponents 1420 (e.g., rows of structured data), domains 1430, extractedkeywords 1440, ratings 1450, and type 1460. The tokenized components1420 can each be classified by domains 1430 based on keywords 1440. Thetokenized components may further be associated with a type 1460 and arating 1450. The type may refer to whether the tokenized component has apositive or negative sentiment (e.g., positive sentiment is (1) andnegative sentiment is (0)). The rating may refer to a domain sentimentvalue. For example, as shown in FIG. 14 , “0” corresponds to an academicdomain with a negative sentiment, “1” corresponds to an academic domainwith a positive sentiment, 2 corresponds to a facilities domain with anegative sentiment, 3 corresponds to a facilities domain with a negativesentiment, etc. The ratings may be used by the system to process eachtokenized component 1420 and to determine the domain scores in step1811.

Referring back to FIG. 12 , the system 100 can search the tokenizedcomponents for specific keywords. In step 1209, the system can identifyand extract the keywords for each tokenized component by performing akeyword search. The keywords in the structured data file can beidentified and extracted. In some embodiments, the system may access akeyword file to be used for performing the keyword extraction based onkeyword(s) provided in the keyword file.

FIG. 15 illustrates an exemplary keyword file 1570. The keyword file1570 includes the keywords associated with each domain. As shown in thefigure, the exemplary domains for the target business can include atleast academics, facilities, and placement. As discussed above, thekeywords can be used to associate a tokenized component 1420 of thestructured data file 1401 with a domain 1430. In some embodiments,keyword files may be generated by synonym search, semantic matches fordomain names, input from SMEs, and the like. In some embodiments, one ormore domains 1430 may be identified using one or more keywords. Thekeywords file can be maintained in the second database 207 of thesystem, for example.

Referring back to FIG. 12 , in step 1211, each tokenized component canbe associated with one or more domains. Once the domains are identified,the system 100 can combine the tokenized components. In step 1213, thesystem 100 can combine the tokenized components with the domains 1430and sentiments to produce a structured data file 1401. In someembodiments, sentiments (e.g., positive or negative sentiments) can beassociated with each tokenized component. For example, a positivesentiment can correspond to a positive review. A negative sentiment cancorrespond to a negative review.

Referring back to FIG. 13A, the unstructured data file 1301 may includea positive sentiment about a university. The unstructured data file 1303may include a negative sentiment about the university. In someembodiments, the system 100 may use separate positive sentiment andnegative sentiment data files. The system may indicate the type of(e.g., positive or negative) sentiment in the type column 1460 of datastructured data file 1401.

The structure data file 1401 can be used to train the domain-scoringmodels to determine benchmark competitor domain scores and/or to predictsentiments (e.g., positive or negative sentiments). FIG. 16 illustratesa flowchart of an exemplary training process 1105 for trainingdomain-scoring models, according to embodiments of this disclosure. Insome embodiments, each domain 1430 may be associated with its owndomain-scoring model. Training a domain-scoring model can include aniterative process that may be repeated for each new set of training datarelated to the industry (e.g., a set of structured data files 1401).Although the process 1105 is illustrated as including the describedelements, it is understood that different order of elements, additionalelements, or fewer elements may be included without departing from thescope of the disclosure.

In step 1601, the system may receive structured data files 1401 thatidentify the domains and sentiments for each tokenized component 1420.The received structured data files 1401 may be used to train thedomain-scoring model. In some embodiments, the structured data file 1401may be associated with one or more non-benchmark competitors. In someembodiments, the structured data files 1401 may be associated with thebenchmark competitor selected in step 337. In some embodiments, thestructured data file 1401 may be associated with the target business'industry. In step 1603, the system can remove tokenized components 1420that do not include a domain and/or sentiment. The remaining tokenizedcomponents 1420 may each be associated with one or more domains.

In step 1605, the tokenized components 1420 can be separated based ondomains. In step 1607, one or more domain-based structured data filescan be created. FIGS. 17A-17C illustrate exemplary domain-basedstructured data files 1707A, 1707B, and 1707C, according to embodimentsof this disclosure. Referring to FIG. 17A, the domain-based structureddata file 1707A corresponds to a structured data file for the placementdomain. The domain-based structured data file 1707A can include thetokenized components, keywords, types, domains, and ratings, as shown inthe figure.

Referring back to FIG. 16 , each of the domain-based structure datafiles may be cleaned (step 1611), pre-processed (step 1613), andvectorized (step 1615). In some examples, the data from the domain-basedstructured data files can be cleaned and self-healed to enhance theaccuracy of the domain-scoring models. Data from the vectorizeddomain-based structure data files can be used to generate a documentterm matrix, in step 1617.

In step 1619, the system 100 may dynamically select a best fit algorithmto train the domain-scoring models. The best fit algorithm may beselected based on one or more factors, such as accuracy, the data pointset, computational costs, and functionality. The best fit algorithm mayinclude, for example, logistic regression, decision trees, randomforests, naïve Bayes, support vector machines, neural networks, and thelike. In step 1621, the selected best fit algorithm may create and/orselect one or more domain-scoring models.

FIG. 18 illustrates a more detailed flowchart of step 1107, where thedomain-scoring models may be applied to a benchmark competitor's data,according to embodiments of this disclosure. Although the process 1107is illustrated as including the described elements, it is understoodthat different order of elements, additional elements, or fewer elementsmay be included without departing from the scope of the disclosure.

In step 1801, the system 100 can receive data (e.g., first data 215)related to the benchmark competitor. In some embodiments, the first data215 may be raw or unstructured data. For example, the first data 215 mayinclude a review, qualitative description, and the like, written aboutthe benchmark competitor.

FIG. 19 illustrates an exemplary unstructured data file 1901 about abenchmark competitor, according to embodiments of this disclosure. Theexemplary unstructured data 1901 can be a qualitative descriptionwritten about the benchmark competitor.

Referring back to FIG. 18 , in step 1803, the unstructured data can betokenized by sentences and combined into a structured data file. Each ofthe tokenized data may form components of the structured data file.

FIG. 20 illustrates exemplary structured data 2001, according toembodiments of this disclosure. Structured data 2001 can be datacorresponding to the unstructured data 1901 that has been processed. Asshown in the figure, the structured data file 2001 can include one ormore of tokenized components 2020, domains 2030, extracted keywords2040, and ratings 2050.

Referring back to FIG. 18 , in step 1805, the system 100 can identifyand extract the keywords 2040 for each tokenized component 2020. Thekeyword search and extraction may be based on keywords provided in akeyword file (e.g., keyword file 1570). In step 1807, each tokenizedcomponent can be associated with one or more domains 2030. In step 1809,the system 100 can perform a sentiment analysis of the data. Thesentiment analysis may be comprise applying the domain-scoring modelsselected in step 1621. In some embodiments, the system 100 can perform aseparate sentiment analysis for each domain. In some embodiments, theoutput of the sentiment analysis can include a structured data file thatincludes the domains and sentiments of each tokenized component of thestructured data.

In step 1811, the system 100 can determine the benchmark competitordomain scores. In some embodiments, the system 100 can applyrelationships (6) and (7) to the structured domain and sentiment data todetermine the benchmark competitor domain scores.

$\begin{matrix}{{TSI} = \frac{p}{n}} & (6) \\{{{Domain}\mspace{14mu}{Score}} = \left\{ \begin{matrix}{p + \left( {{TSI}*{tp}} \right)} \\{p - \left( {{TSI}*{tn}} \right)}\end{matrix} \right.} & (7)\end{matrix}$where TSI is the sentiment index, p is the mid-point of the scale of thedomain score (e.g., 1-5, 1-1-10, 1-100, etc.), n is the total number oftokenized components, tp is the total number of positive sentimentcomponents, and tn is the total number of negative sentiment tokens.

Table 10 shows the domain scores determined based on the structured data2001. The domain scores shown in Table 10 are based on a 5 point score.

TABLE 10 Domain Score Academics 3.78 Facilities 1.02 Placement 5

As discussed above, the system 100 can provide a graphicalrepresentation of the results (e.g., initial performance score of thetarget business, target business domain scores, benchmark performancescore of the benchmark competitor, and benchmark competitor domainscores) to the user via the website 201. The graphical representationcan be a spider graph, a comparative bar chart, and the like. Thegraphical representation may allow the user (e.g., a business leader atthe target business) to visualize and compare their target business'performance against the benchmark competitor.

Exemplary Process Decomposition

In some embodiments, the system 100 may be configured to provide anassessment of a target business by assessing the performances ofindividual processes or criteria unique to the target business. In someembodiments, the assessment is based on performance drivers, withoutlimitation, such as cost, quality, and time (CQT). The system 100 mayalso be configured to provide recommendations to a target business byassessing criteria unique to the target business. In some embodiments,the recommendations are based on pain-points. By assessing a targetbusiness based on individual processes and/or criteria unique to thetarget business, the system 100 can advantageously perform an assessmentor provide recommendations more suitable for the target business using abottom up approach, reducing costs and need for additional assessments(e.g., for hiring SMEs, for hiring consultants, etc.).

As discussed in more detail below, the system 100 may perform one ormore of: determining an updated performance score of a target businessand/or recommending KPIs for improving the target business. For example,the target business may be a user's business.

Exemplary Performance Deviation Reestablishment

FIG. 21 illustrates a flowchart of process 2100 for determining anupdated performance score of the target business, according toembodiments of this disclosure. Although the process 2100 is illustratedas including the described elements, it is understood that differentorder of elements, additional elements, or fewer elements may beincluded without departing from the scope of the disclosure.

As illustrated, the process 2100 includes providing a first input (step2102). For example, the input is a set of answers to a questionnaireanswered by a user (e.g., a leader of the target business). In responseto receiving the first input, an initial performance score of the targetbusiness is determined (step 2104). For example, the initial performancescore of the target business is determined by the system 100, asdiscussed above.

The process 2100 may include providing a second input (step 2106). Thesecond input may be provided to the system 100. For example, the secondinput is a list of processes associated with the target business, scores(e.g., experience, efficiency, data quality, automation, etc.)associated with each of the processes, and information (e.g., owner ofthe process, location of the process, priority of the process, etc.)associated with each of the processes. In some examples, the secondinput is at least in part provided by someone in the target businessassociated with the process (e.g., a manager, a person whose jobdescription is associated with the process, a process owner, etc.). Insome examples, the second input is at least in part based on industrystandards. In some embodiments, the list of processes is hierarchicaland may include up to a five-level hierarchy. It is understood that thefive-level hierarchy is merely exemplary; different numbers of levels ofprocesses in the hierarchy may exist without departing from the scope ofthe disclosure.

In response to receiving the second input, process scores are determined(step 2108). For example, based on the second input, the system 100determines process scores for each of the processes in the second input.The process scores may indicate an effectiveness of a correspondingprocess and may help a user identify effective or ineffective processesto identify changes (e.g., room for growth) at the target business.

As illustrated, the process 2100 includes providing an assessment data(step 2110). For example, the assessment data is associated with thefirst input (e.g., from step 2102). In some embodiments, providing theassessment data includes cleaning up (e.g., reorganizing, updating forconsistency, updating for compatibility, etc.) information associatedwith the first input before further processing of the assessment data isperformed. In some embodiments, the assessment data is data associatedwith the system 100 (e.g., assessment 1 of FIG. 1 ). For example, theassessment data may be benchmarking data.

The process 2100 includes providing a capability process data (step2112). For example, the capability process data is associated with thesecond input (e.g., from step 2106). In some embodiments, providing thecapability process data includes cleaning up (e.g., reorganizing,updating for consistency, updating for compatibility, etc.) informationassociated with the second input before further processing of thecapability process data is performed. In some embodiments, thecapability process data is data associated with the system 100 (e.g.,process decomposition 2 of FIG. 1 ).

In response to receiving the assessment data and the capability processdata, the process 2100 may include determining a performance scoredeviation (step 2114). In some embodiments, an updated performance scoreof the target business is determined (step 2116) based on theperformance score deviation and an initial performance score of thetarget business (e.g., from step 2104, using methods described herein).The determination of the performance score deviation and thedetermination of the updated performance score are described in moredetail herein.

As an exemplary advantage, using the processes described herein, a moreaccurate performance score of the target business (e.g., an updatedperformance score of the target business) may be determined. As aresult, a target business may be more accurately assessed. For example,as illustrated with the following examples, the updated performancescore of the target business accounts for performance drivers such ascost, quality, and time, criticality of each of the performance drivers,and information about processes of the target business. Additionally, asillustrated with the following examples, by adjusting the weightages ofcomponents of the performance score of the target business based on theneeds of the target business or industry, the system 100 may beadvantageously used across different industries to provide more accurateassessments of different target businesses using the same system.

FIG. 22 illustrates a flowchart of process 2200 for determining anupdated performance score of the target business, according toembodiments of this disclosure. Although the process 2200 is illustratedas including the described elements, it is understood that differentorder of elements, additional elements, or fewer elements may beincluded without departing from the scope of the disclosure.

As illustrated, the process 2200 includes providing an assessment data(step 2202). For example, the assessment data 2300 is the data describedwith respect to step 2110.

FIG. 23 illustrates an exemplary assessment data 2300, according toembodiments of this disclosure. In some embodiments, the assessment data2300 includes information relating to an input (e.g., response toassessments or questions provided to a leader of the target business,first input from step 2102) to the system 100. In some embodiments,information in the assessment data 2300 is used to determine an initialperformance score of the target business (e.g., step 2104). In someembodiments, when determining an updated performance score of the targetbusiness, the assessment data 2300 may have already been saved when aninitial performance score of the target business was determined, so auser does not need to answer the questions again. In some embodiments,new or additional information for the assessment data 2300 is provided.

The assessment data 2300 may include domains 2302, question identifiers2304, questions 2306, responses 2308, and descriptions 2310. The domains2302 on each row is a domain corresponding to a question on the row. Forexample, the target business is a health science business, and thedomains 2302 include procurement, inventory, and compliance. It isunderstood that the illustrated domains are exemplary. Different numbersof domains or different domains may exist without departing from thescope of the disclosure.

Each of the question identifiers 2304 may be a code (e.g., a number)corresponding to a particular question 2306. The question identifiers2304 may be determined automatically or defined by a user or anadministrator. The question identifiers 2304 may advantageously allowquestions to be accessed or recognized (e.g., by the system 100) moreefficiently (e.g., compared to identification using the text of aquestion). The questions 2306 may be questions provided to a leader ofthe target business for assessing the target business (e.g., todetermine an initial performance score of the target business).

The responses 2308 may be responses to questions 2306. The questions2306 may be associated with an assessment of a target business. Asillustrated, the responses are numbers quantifying (e.g., how well, towhat extent, etc.) an otherwise textual response to a question. Forexample, the numbers may range from 0 to 5, where 0 represents thelowest quantity (e.g., poorest, non-existent) and 5 represents thehighest quantity (e.g., best). By quantifying the responses, theresponses may be used to determine an initial performance score of thetarget business and the target business domain weights or performancedriver weights to determine an updated performance score of the targetbusiness (as described in more detail below), allowing quantitativeassessment of the target business. Although the responses areillustrated as being numeric, it is understood that other types ofresponses may exist. For example, the responses may be textual.

The descriptions 2310 may be descriptions provided by the user answeringthe questions 2306. The descriptions 2310 may provide additionalinformation about the responses 2308 provided by the user. Thedescriptions 2310 may be used to determine an updated performance scoreof the target business. For example, information related to performancedrivers (e.g., cost, quality, time) may be extracted (e.g., using NLP)to determine performance driver weights for determining the updatedperformance score of the target business.

Although the assessment data 2300 is illustrated as being organized incolumn and rows and including the described information, it isunderstood that the illustration of the assessment data 2300 is merelyexemplary. It is understood that the assessment data 2300 may berepresented in other forms, may be organized, or may include differentinformation in a different manner without departing from the scope ofthe disclosure.

Returning back to FIG. 22 , the process 2200 includes providingcapability process data (step 2204). For example, the capability processdata 2400 is the data described with respect to step 2112.

FIG. 24 illustrates an exemplary capability process data 2400, accordingto embodiments of this disclosure. In some embodiments, the capabilityprocess data 2400 includes information relating to an input (e.g.,information about processes provided by process owners, second inputfrom step 2106 of FIG. 21 ) to the system 100. In some embodiments,information in the capability process data 2400 is used to determineprocess scores (e.g., step 2108 of FIG. 21 ). In some embodiments, whendetermining an updated performance score of the target business (step2116 of FIG. 21 ), the capability process data 2400 may have alreadybeen saved when process scores were determined, so a user does not needto provide the information (e.g., process-related information) again. Insome embodiments, new or additional information for the capabilityprocess data 2400 is provided (e.g., when updated information about aprocess becomes available).

The capability process data 2400 may include process identifiers 2402,process titles 2404, process scores 2406, and responses 2408. Theprocess identifiers 2402 may be a code (e.g., numbers) corresponding toa particular process. A period in a process identifier may indicate alevel of a corresponding process. Additional periods in the identifierindicate that the corresponding process belongs to a lower level (e.g.,as defined in step 2106 of FIG. 21 , when second input is provided). Forexample, the process “identify store location demography” is a level 2process, as indicated by the identifier “1.1,” which has one period. Theprocess identifiers 2402 may allow the system 100 to efficiently accessor review all the processes of a target business (e.g., for determiningthe number of occurrences of performance drivers (e.g., step 2208 ofFIG. 22 or step 2508 of FIG. 25 )). It is understood that theillustrated format for the process identifiers is not meant to belimiting; other process identifier formats may exist.

The process titles 2404 may be a title of a corresponding process. Thetitle of the process may be provided by a user (e.g., a process owner)when an input (e.g., second input) to the system 100 is provided (e.g.,step 2106 of FIG. 21 ). The title of the process may also beautomatically determined (e.g., to align with industry standards, toform a more efficient process structure, etc.). As illustrated, theprocess titles 2404 are separated by levels, and a corresponding titlefor a corresponding level is shown. For example, for process 1.1.1 under“Facilities Management,” the level 1 title for the corresponding processis “Facilities Management,” the level 2 title for the correspondingprocess is “Identity Store Location,” and the level 3 title for thecorresponding process is “Demographic Impacts of Pandemic.” While thisexample illustrates five process levels and five corresponding processtitles, it is understood the number of process levels and thecategorization of process titles are not meant to be limiting.

The process scores 2406 may be a score indicative of a performance of aprocess. For example, the process scores 2406 are determined at step2108 of FIG. 21 . As illustrated, the process “Facilities Management”has a process score 2406 of 3.4, and the process “M&A Due DiligencePlanning” has a process score 2406 of 3.65. The process scores 2406 maybe determined by responses 2408 corresponding to a respective process,which may be provided by a user (e.g., a process owner) to the system100 (e.g., at step 2106 of FIG. 21 ). For example, the process score2406 for “Facilities Management” may be determined based on theresponses 2408 for the process scores 2406 of the lower level“Facilities Management” processes. Although higher level processes areillustrated as having process scores 2406, it is understood that lowerlevel processes may also be scored.

Although the capability process data 2400 is illustrated as beingorganized in column and rows and including the described information, itis understood that the illustration of the capability process data 2400is merely exemplary. It is understood that the capability process data2400 may be represented in other forms, may be organized in a differentmanner, and/or may include different information without departing fromthe scope of the disclosure.

Returning to FIG. 22 , the process 2200 may include, for each domain,determining the occurrences of performance drivers in the assessmentdata (step 2206). In some embodiments, the step 2206 also includesidentifying domains of the target business in the assessment data.

Each time an occurrence of a performance driver is found for arespective domain, a value corresponding to the number of occurrences ofthe performance driver and the respective domain may be incremented byone (e.g., the number of occurrences of cost for the inventory domainincrements by one). In some examples, if a number of occurrence of aperformance driver is greater than one, the number of occurrence of theperformance driver is set to one, instead of the actual number ofoccurrence of the performance driver. At the completion of step 2206,the total number of occurrences of each performance driver for eachdomain may be stored and used to determine an aggregate performancedriver weight for each domain associated with the assessment data 2300.This step may also include determining domains of the data in theassessment data 2300. In the health science business example, the system100 determines whether a respective portion of assessment data (e.g., anentry in the assessment data 2300) is associated with at least one ofthe procurement, inventory, and compliance domains. In some embodiments,using NLP, domains of the assessment data 2300 are determined. Forexample, an AI NLP library, such as WordNet, BeautifulSoup, and Spacy,are used (e.g., a synonym search) to determine the associated domain. Insome embodiments, the domains in the assessment data 2300 have beenidentified previously (e.g., when an initial performance score of thetarget business is determined).

The performance drivers may include, without limitation, cost, quality,and time. In some embodiments, using NLP, the occurrences of theperformance drivers are determined from the assessment data 2300. Forexample, an AI NLP library, such as WordNet, BeautifulSoup, and Spacy,are used (e.g., a synonym search) to determine the occurrences of theperformance drivers in the assessment data 2300. As another example, anonline database (e.g., including terms related to the performancedrivers) is used to determine the occurrences of the performance driversin the assessment data 2300.

In examples where the performance drivers include cost, quality, andtime, for each domain (e.g., procurement, inventory, compliance), theoccurrences of cost, quality, and time in assessment data may beassociated with the system 100 (e.g., assessment 1 of FIG. 1 ) and aredetermined using NLP. For example, the data may be responses provided bya leader to questions, a cleaned-up version of the responses, etc. Thenumber of occurrences of the performance drivers in the assessment data2300 may be used to determine weights of performance drivers associatedwith the assessment data 2300, as described in more detail in subsequentsteps.

For example, an assessment of a target business includes a question “howwell does your organization procure the most cost-effective drugs in theright quantities?” The response to the question is “the organization hasinitiated integration of technology drive procurement list maintenancein order to ensure continuous access to products.” In this example, theresponse to the question includes terms related to cost and time (e.g.,based on the NLP). The occurrence of cost would be one, the occurrenceof time would be one, and the occurrence of quality would be zero. Thenumber of occurrences of the cost and time in this example may be usedto determine weights of performance drivers, as described in more detailin subsequent steps.

As another example, an assessment of a target business includes aquestion “how well does your organization select reliable suppliers ofhigh-quality products at right cost?” The response to the question is“reliable suppliers of high-quality products are pre-selected on thebasis of strategic guidelines and additional active quality assuranceprograms involving both surveillance and testing across the drugsspectrum.” In this example, the response to the question includes termsrelated to cost, quality, and time (e.g., based on the NLP). Theoccurrence of cost would be one, the occurrence of quality would be one,and the occurrence of time would be one. The number of occurrences ofthe cost, quality, and time in this example may be used to determineweights of performance drivers, as described in more detail insubsequent steps.

As exemplary advantages, using NLP to determine a domain (e.g.,inventory, procurement, and compliance) allows the assessment data itemsto be more accurately categorized by domains. In some embodiments, usingNLP to determine the occurrences of performance drivers (e.g., cost,quality, and time) allows information related to performance drivers tobe more accurately extracted, and a more accurate updated performancescore of the target business may be determined. For example, a user maynot necessarily provide an input to create the assessment data 2300 withthe domains in mind (e.g., the user may not be mindful about providingdomain-specific information). As another example, a user may notnecessarily provide an input to create the assessment data 2300 with theperformance drivers in mind (e.g., the user may not be mindful aboutproviding information about cost, quality, and time while providing theinput). Using NLP to extract information about the performance driversallows a user to more accurately provide the input (e.g., in an unbiasedmanner, without being influenced by domain categorization or performancedrivers, etc.) because the user does not need to expressively inputinformation about the domains or performance drivers.

The process 2200 of FIG. 22 may include, for each domain and eachprocess, determining the occurrences of performance drivers in thecapability process data (step 2208). In some embodiments, the step 2208includes identifying domains of the target business in the capabilityprocess data. This step is described in more detail with respect to FIG.25 .

FIG. 25 illustrates a flowchart of process 2500 for determining theoccurrences of performance drivers in the capability process data,according to embodiments of this disclosure. Although the process 2500is illustrated as including the described elements, it is understoodthat different order of elements, additional elements, or fewer elementsmay be included without departing from the scope of the disclosure.

As illustrated, the process 2500 includes providing capability processdata (step 2502). For example, the capability process data is the datadescribed with respect to step 2112 (FIG. 21 ) or step 2204 (FIG. 22 ).The process 2500 may include for each process level, identifying aprocess (step 2504). For example, each of the processes at each processlevel in the capability process data 2400 is identified. As an example,for process level 3, the processes “Demographic Impacts of Pandemic,”“Store Buying Pattern History,” and “Store Expense Procurement Analysis”may be identified. Alternatively, the processes may be identifiedwithout sequentially searching through the process levels.

For each identified process at each level (e.g., each process listed inthe capability process data 2400), the process 2500 includes determiningwhether the identified process is associated with a domain (step 2506shown in FIG. 25 ). In some embodiments, the step 2506 also includesidentifying domains of the target business in the capability processdata. In the health science business example, for each identifiedprocess at each level, the system 100 determines whether the identifiedprocess is associated with at least one of the procurement, inventory,and compliance domains. In some embodiments, using NLP, an associateddomain for each of the identified process is determined. For example, anAI NLP library, such as WordNet, BeautifulSoup, and Spacy, are used(e.g., a synonym search) to determine the associated domain. Anassociated domain may be determined based on the number of occurrencesof a domain for each process (e.g., the domain that occurs the most fora particular process during a search). For example, for a process“Review Annual Operational Compliance,” the number of occurrence of theCompliance domain is one, and the numbers of occurrence of the inventoryand procurement domains are zero. In some embodiments, the steps ofidentifying whether a process is associated with a domain are repeatedfor all process level until all processes in all levels have beenreviewed.

The process 2500 may include, for each domain in the identifiedprocesses in each of the process levels, determining the number ofoccurrences of performance drivers in the data (step 2508). Each time anoccurrence of a performance driver is found for a respective domain, avalue corresponding to the number of occurrences of the performancedriver and the respective domain may be incremented by one (e.g., thenumber of occurrences of cost for the inventory domain increments byone). In some examples, if a number of occurrence of a performancedriver is greater than one, the number of occurrence of the performancedriver is set to one, instead of the actual number of occurrence of theperformance driver. At the completion of step 2508, the total number ofoccurrences of each performance driver for each domain may be stored andused to determine an aggregate performance driver weight for each domainassociated with the assessment data. For example, the domains include,without limitation, procurement, inventory, and compliance domains, andthe performance drivers include, without limitation, cost, quality, andtime. In some embodiments, using NLP, the occurrences of the performancedrivers are determined from the identified processes for each processlevel for each of the domains (e.g., from the capability process data).For example, an AI NLP library, such as WordNet, BeautifulSoup, andSpacy are used (e.g., a synonym search) to determine the occurrences ofthe performance drivers from the identified processes for each processlevel for each of the domains (e.g., from the capability process dataassociated with the system 100 from, e.g., step 2112 of FIG. 21 ).

For example, a process of a target business includes “review legislativechanges from prior year.” In this example, the process includes termstime (e.g., based on the NLP). The occurrence of time would be one, theoccurrences of cost and time would be zero. The number of occurrences ofthe cost and time in this example may be used to determine weights ofperformance drivers, as described in more detail in subsequent steps.

As exemplary advantages, using NLP to determine a domain (e.g.,inventory, procurement, and compliance domains) allows the capabilityprocess data items to be more accurately categorized by domains. UsingNLP to determine the occurrences of performance drivers (e.g., cost,quality, and time) allows information related to performance drivers tobe more accurately extracted, and a more accurate updated performancescore may be determined. For example, a user may not necessarily providean input to create the capability process data with the domains in mind(e.g., the user may not be mindful about providing domain-specificinformation). As another example, a user may not necessarily provide aninput to create the capability process data with the performance driversin mind (e.g., the user may not be mindful about providing informationabout cost, quality, and time while providing the input). Using NLP toextract information about the performance driver allows a user to moreaccurately provide the input (e.g., in an unbiased manner, without beinginfluenced by domain categorization or performance drivers) because theuser does not need to expressively input information about the domainsor the performance drivers.

As another example, an online database (e.g., including terms related tothe performance drivers) is used to determine the occurrences of theperformance drivers from the identified processes for each process levelfor each of the domains (e.g., from the capability process data).

In examples where the performance drivers include cost, quality, andtime, for each domain (e.g., procurement, inventory, and compliance),the occurrences of cost, quality, and time in the capability processdata may be associated with the system 100 and are determined using NLP.The number of occurrences of the performance drivers in the capabilityprocess data may be used to determine particular performance driversassociated with the capability process data, as described in more detailin subsequent steps. In some embodiments, after the occurrences ofperformance drivers in the data are determined, the determinedoccurrences are used for subsequent processing (e.g., determining aperformance driver weight)

Although the process 2500 is illustrated as having step 2508(determining the occurrences of performance drivers) as following step2506 (determining whether the identified process is associated with adomain for each process level), it is understood that step 2508 may notnecessary be performed after all processes at all process levels havebeen reviewed. For example, in some embodiments, the step 2508 may beperformed while step 2506 is concurrently being performed. That is, thestep 2508 may performed for identified processes while step 2506 isbeing performed for processes that have not been reviewed.

FIG. 26 illustrates an exemplary classified process data 2600, accordingto embodiments of this disclosure. The classified process data 2600 mayinclude the processes from the capability process data, and domainsassociated with the processes are identified in the classified processdata 2600. In some embodiments, the exemplary processes and theircorresponding domains are identified using process 2500 or step 2208 ofprocess 2200.

The classified process data 2600 may include process titles 2602,process scores 2604, process identifiers 2606, domain levels 2608, anddomains 2610. The process titles 2602 may correspond to process titles2404 of the capability process data 2400 (FIG. 24 ), which may be titlesof the processes. In some embodiments, the process titles may beorganized according to a process level of a particular process. Theprocess scores 2606 may correspond to process scores 2406 of thecapability process data 2400 (FIG. 24 ), which may be process scoresindicating the performance of the processes (e.g., as determined in step2108 of FIG. 21 ).

The process identifiers 2606 may be a code (e.g., a number) identifyinga set of processes. For example, all “Risk & Compliance” processes havea process identifier of “0,” and all “MOR” processes have a processidentifier “1.” It is understood that using a code (e.g., a number) toidentify a set of processes is not meant to be limiting.

The domain level 2608 may be the process level of a process title (e.g.,process title 2602) that causes the process to be identified asbelonging to the corresponding domain 2610. The domain level may beidentified using NLP. For example, the domain level of the process“Review Annual Operational Compliance” is level three because the levelthree title of the process, “Review Annual Operational Compliance,”causes the process to be identified as belonging to the compliancedomain (e.g., domain_found is three). As another example, the domainlevel of the process “Perform Gap Analysis” is level two because thelevel two title of the process, “Risk & Compliance,” causes the processto be identified as belonging to the Compliance domain (e.g.,domain_found is three).

The domains 2610 may indicate a domain that a process is associatedwith. As illustrated, the domains 2610 are represented using a code(e.g., a number). However, the use of a code (e.g., a number) torepresent a domain is not meant to be limiting. For example, all “Risk &Compliance” processes are determined (e.g., using process 2500 or step2208 of process 2200) to be associated with domain “3” (e.g.,compliance), and some of the “MOR” processes are determined to beassociated with domain “0,” “1,” or “2” (e.g., one of inventory andprocurement). A domain of a process may be determined based on theoccurrences of a domain for the process (e.g., determined during step2208 of process 2200 or step 2506 of process 2500).

In some examples, the domain “0” may be associated with another domain.The other domain may be a domain that does not belong (e.g., asdetermined using process 2500 or step 2208 of process 2100) to anypredetermined domain (e.g., does not belong to the compliance,inventory, or procurement domains), or the corresponding process maybelong to more than one domain. By identifying an associated domain witha process, a component of an updated target business domain score may bedetermined, allowing the performance of the target business in thecorresponding domain to be quantified and accounted for in the updatedperformance score of the target business.

Although the classified process data 2600 is illustrated as beingorganized in column and rows and including the described information, itis understood that the illustration of the classified process data 2600is merely exemplary. It is understood that the classified process data2600 may be represented in other forms, may be organized in a differentmanner, and/or may include different information without departing fromthe scope of the disclosure.

Returning to FIG. 22 , the process 2200 may include separating theassessment data and the capability process data by identified domains(step 2210). In the health science business example, the domains includeinventory, procurement, and compliance domains, and the first andcapability process data are separated by these domains. For theassessment data, the domains may be identified at step 2206. For thecapability process data, the domains may be identified at step 2208 orstep 2506 of process 2500. It is understood that “separating” does notnecessarily mean separate files are created for each data and eachdomain. For example, a data may be partitioned by domain. After theassessment data and the capability process data have been separated bydomains and the occurrences of the performance driver have beendetermined, performance driver weights may be determined, as describedwith respect step 2212 (FIG. 22 ).

Although the process 2200 illustrates the step of separating the data bydomains as following steps of determining the occurrences of performancedrivers in the assessment data (e.g., step 2206) and in the capabilityprocess data (e.g., step 2208 (FIG. 22 ) or step 2508 in process 2500),it is understood that the step of separating the data by domains may notnecessarily be performed after the occurrences of the performancedrivers are determined.

For example, a portion of the step 2210 corresponding to the assessmentdata may be performed concurrently with or before step 2206. That is,the assessment data may be separated by domains before the occurrencesof the performance drivers are determined in the assessment data. Asanother example, a portion of the step 2210 corresponding to thecapability process data may be performed concurrently with step 2208 orconcurrently with/before step 2508 in process 2500. That is, thecapability process data may be separated by domains before theoccurrences of the performance drivers are determined in the capabilityprocess data.

As illustrated, the process 2200 includes determining performance driverweight(s) (step 2212). In the health science example, the performancedrivers include cost, quality, and time. In this example, during thisstep, a weight associated with each of cost, quality, and time isdetermined for each of items in the assessment data (e.g., questions,responses to questions, etc.) and for each of the items in thecapability process data (e.g., processes).

The performance driver weights may be based on the number of occurrencesof the performance drivers (e.g., the number of occurrences of aperformance driver for each domain (e.g., from step 2206 (FIG. 22 ) orfrom step 2508 of process 2500)). For example, a higher number ofoccurrences of a performance driver may yield a higher correspondingperformance driver weight, and a lower number of occurrences of aperformance driver may yield a lower corresponding performance driverweight. The performance driver weights may be represented by percentageweights, as described with respect to FIGS. 27 and 28 . The performancedriver weights may be used to determine aggregate performance driverweights for the respective data, as described with respect to step 2214of process 2200.

FIG. 27 illustrates assessment performance driver data 2700 includingexemplary performance driver weights for domains associated withassessment data, according to embodiments of this disclosure. In someembodiments, the performance driver weights for domains associated withassessment data are determined using step 2212 of process 2200. Theassessment performance driver data 2700 may include domains 2702,questions 2704, the occurrences of the performance drivers 2706,performance driver weights 2708, and domain scores 2710.

The domains 2702 may be identified using step 2206 after the assessmentdata is provided. Although the components (e.g., questions) of theassessment performance driver data 2700 are illustrated as beingorganized by domains, it is understood that the organization is merelyexemplary; the components of the assessment performance driver data 2700may not be organized by domain. The questions 2704 may be questions 2306from the assessment data 2300. The questions 2704 may be questions givento a leader of the target business when the first input is provided withrespect to step 2102 (FIG. 21 ) to generate an initial performance scoreof the target business.

The occurrences of the performance drivers 2706 may be total occurrencesof the performance drivers, determined from step 2206 (FIG. 22 ). Forexample, each time an occurrence of a performance driver is identified,the number of occurrences of the performance driver corresponding to theparticular performance driver (e.g., cost, quality, time) and theparticular domain (e.g., inventory, procurement, compliance) isincremented until a search for performance drivers in the assessmentdata has been completed. For example, for the “Procurement” domain andthe question “To what extent is data analytics use . . . ,” the totalnumber of occurrences of cost is one, the total number of occurrences ofquality is zero, and the total number of occurrences of time is one.

The performance driver weights 2708 may be computed based on the numberof occurrences of the performance drivers 2706. The performance driverweights 2708 may be represented as percentages, and the percentages maybe weights used determine an aggregate performance driver weight. Forexample, a performance driver weight for a particular performance driverat a particular domain is based on the total number of occurrences ofthe particular performance driver at the particular domain. As anexample, for the “Procurement” domain and the question “To what extentis data analytics use . . . ,” the cost weight is 40%, the qualityweight is 20%, and the time weight is 40%. In some embodiments, theperformance driver weights are determined based on a ratio between thenumbers of occurrences of the performance drivers. For example, theratio between the numbers of occurrences of cost:quality:time is 1:0:1.Based on the ratio, a higher weight (e.g., percentage) is determined forcost and time, and a lower weight (e.g., percentage) is determined forquality. As another example, if the ratio between the numbers ofoccurrences of cost:quality:time is 1:1:1 or 0:0:0, the cost, quality,and time weights are equal at 33.33%. Table 11 illustrates a matrixincluding exemplary performance driver weights for different occurrencesof the performance drivers:

TABLE 11 Performance Driver(s) A B C A only 50% 25% 25% B only 25% 50%25% C only 25% 25% 50% A and B 40% 40% 20% B and C 20% 40% 40% A and C40% 20% 40% A and B and C 33.33% 33.33% 33.33% None 33.33% 33.33% 33.33%

For example, the performance drivers A, B, and C are cost, quality, andtime respectively. As an example, for the “Compliance” domain and thequestion “Does the management of pharmac . . . ,” the ratio of thenumbers of occurrences of cost:quality:time is 0:0:1 (e.g., a ratio ofA:B:C). From the exemplary performance driver weight matrix, a costweight would be 25%, a quality weight would be 25%, and a time weightwould be 50%.

Although specific values of the performance driver weights correspondingto different occurrences of the performance drivers are described, it isunderstood that these values are not meant to be limiting. In someembodiments, depending on the application, the values of the performancedriver weights may be higher or lower for the same numbers ofoccurrences of the performance drivers, compared to the examplesdescribed herein. For example, in a different application, a ratio ofA:B:C=0:0:1 may yield a weight of 20% for A, 20% for B, and 60% for C.

Computation of the performance driver weights advantageously may lead todetermining a more accurate updated performance score of the targetbusiness. By determining the occurrences of a performance driver foreach domain and computing the performance driver weights, the importanceof a performance driver may be quantitatively factored in using theperformance driver weights while determining the updated performancescore. For example, if the number of occurrences of cost is higher forthe inventory domain, a corresponding cost weight for the inventorydomain may be scaled accordingly (e.g., determined to have a higherpercentage) so that the importance of the cost performance driver isreflected in the updated performance score of the target business,providing a more accurate assessment of the target business' performancein this situation.

The domain score 2710 may be representative of a target business'performance in the respective domain, as described with respect to thesystem 100. For the sake of brevity, the description of the domain scorewould not be provided again here.

Although the assessment performance driver data 2700 is illustrated asbeing organized in column and rows and including the describedinformation, it is understood that the illustration of the assessmentperformance driver data 2700 is merely exemplary. It is understood thatthe assessment performance driver data 2700 may be represented in otherforms, may be organized in a different manner, and/or may includedifferent information without departing from the scope of thedisclosure.

FIG. 28 illustrates capability performance driver data 2800 includingexemplary performance driver weights for domains associated with acapability process data, according to embodiments of this disclosure. Insome embodiments, the performance driver weights for domains associatedwith a capability process data are determined using step 2212 (FIG. 22). The capability performance driver data 2800 may include processtitles 2802, process scores 2804, domains 2806, occurrences of theperformance drivers 2808, and performance driver weights 2810.

The process titles 2802 may correspond to process titles 2602 of theclassified process data 2600, the process scores 2804 may correspond toprocess scores 2604, and domains 2806 may correspond to domains 2610.For the sake of brevity, elements described with respect to theclassified process data 2600 that are similar to the capabilityperformance driver data 2800 are not provided again here.

The occurrences of the performance drivers 2808 may be the totaloccurrences of the performance drivers determined from step 2208 (ofprocess 2200) or step 2508 (of process 2500) for each process. Forexample, for a particular process, each time an occurrence of aperformance driver is identified, the number of occurrences of theperformance driver corresponding to the particular performance driver(e.g., cost, quality, time) and the particular domain (e.g., inventory,procurement, compliance) is incremented until a search for performancedrivers in the capability process data has been completed. For example,for the “Review Legislative Changes for Prior Year” process, a totalnumber of occurrences of cost is zero, a total number of occurrences ofquality is zero, and a total number of occurrences of time is one.

The performance driver weights 2810 may be computed based on theoccurrences of the performance drivers 2808. The performance driverweights 2810 may be represented as percentages, and the percentages maybe weights used determine an aggregate performance driver weight. Forexample, a performance driver weight 2810 for a particular performancedriver at a particular domain is based on a total number of occurrencesof the particular performance driver at the particular domain. As anexample, for the “Review Legislative Changes for Prior Year” process,the cost weight is 25%, the quality weight is 25%, and the time weightis 50%. In some embodiments, the performance driver weights aredetermined based on a ratio between the numbers of occurrences of theperformance drivers. For example, the ratio between the numbers ofoccurrences of cost:quality:time is 1:0:1. Based on the ratio, higherweights (e.g., percentages) are determined for cost and time, and alower weight (e.g., percentage) is determined for quality. As anotherexample, if a ratio between the numbers of occurrences ofcost:quality:time is 1:1:1 or 0:0:0, the cost, quality, and time weightsare equal at 33.33%.

Referring back to Table 11, for the “Review Legislative Changes forPrior Year” process, a ratio of the numbers of occurrences ofcost:quality:time is 0:0:1 (e.g., a ratio of A:B:C). From the exemplaryperformance driver weight matrix of Table 11, the cost weight would be25%, the quality weight would be 25%, and the time weight would be 50%.

Although specific performance driver weights corresponding to differentoccurrences of the performance drivers are described, it is understoodthat these values are not meant to be limiting. In some embodiments,depending on the application, the performance driver weight values maybe higher or lower for the same numbers of occurrences of theperformance drivers, compared to the examples described herein. Forexample, in a different application, a ratio of A:B:C=0:0:1 may yield aweight of 20% for A, 20% for B, and 60% for C.

Computation of the performance driver weights advantageously allows amore accurate updated performance score to be determined. By determiningoccurrences of a performance driver for each domain and computing theperformance driver weights, a performance driver's contribution to theupdated performance score may be emphasized or a de-emphasized. Forexample, if the number of occurrences of cost is higher for theinventory domain for a process, a corresponding cost weight for theinventory domain for the process would be scaled accordingly (e.g.,determined to have a higher percentage) to more emphasize the costperformance driver in the updated performance score, which provides amore accurate assessment of a business in this situation.

Although the capability performance driver data 2800 associated withexemplary performance driver classification by domains associated with acapability process data is illustrated as being organized in column androws and including the described information, it is understood that theillustration of the capability performance driver data 2800 is merelyexemplary. It is understood that the capability performance driver data2800 may be represented in other forms, may be organized in a differentmanner, and/or may include different information without departing fromthe scope of the disclosure.

Returning to FIG. 22 , the process 2200 may include determiningaggregate performance driver weights (step 2214). Based on theperformance driver weights determined from step 2212, aggregateperformance driver weights may be determined for the assessment data andthe capability process data. In the health science business example, thedomains include procurement, inventory, and compliance, and theperformance drivers include cost, quality, and time. In this example,nine aggregate performance driver weights may be determined based on theperformance driver weights from step 2212. For each of the procurement,inventory, and compliance domains, the cost weights are summed, thequality weights are summed, and the time weights are summed. For eachdomain, percentage of [sum of cost weights/(sum of cost, quality, andtime weights)], percentage of [sum of quality weights/(sum of cost,quality, and time weights)], and percentage of [sum of time weights/(sumof cost, quality, and time weights)] are calculated. Each percentage isapplied to a corresponding domain score (e.g., domain score 2710 in FIG.27 ). For example, the procurement domain score is 3.2. A costpercentage is multiplied to the procurement domain score to obtain theaggregate cost weight for the procurement domain. A quality percentageis multiplied to the procurement domain score to obtain the aggregatequality weight for the procurement domain. A time percentage ismultiplied to the procurement domain score to obtain the aggregate timeweight for the procurement domain.

For example, the aggregate performance driver weights associated withthe assessment data may be represented as follows:

amp_proc=(1.056, 1.0739, 1.056)

amp_inv=(0.6273, 0.8205, 1.0477)

amp_comp=(1.254, 1.083, 1.444)

“amp_proc” may be a set of aggregate performance driver weights for theprocurement domain, “amp_inv” may be a set of aggregate performancedriver weights for the inventory domain, and “amp_comp” may be a set ofaggregate performance driver weights for the compliance domain. For eachset of aggregate performance driver weights, each value corresponds to aperformance driver. For example, the first value of each set correspondsto cost, the second value of each set corresponds to quality, and thethird value of each set corresponds to time.

The aggregate performance driver weights advantageously allows a moreaccurate updated performance score to be determined. The aggregateperformance driver weights reflect emphasis or a de-emphasis of aparticular performance driver based on an occurrence of the particularperformance driver for a domain. For example, if the number ofoccurrences of cost is higher for the inventory domain, the aggregatecost weight would be higher and reflect an emphasis on the costperformance driver, which would provide a more accurate assessment of abusiness in this situation.

In some embodiments, before aggregate performance driver weights for thecapability process data are determined and after step 2212, an aggregateperformance driver weight for the different processes may be generated.The performance driver weights may be combined by domains to generate anaggregate performance driver weight for the capability process data. Inthe health science business example, the domains include procurement,inventory, and compliance, and the performance drivers include cost,quality, and time. For each set of processes (e.g., all processes undera same process level 1 title, all “Facilities Management” processes inFIG. 24 ), the cost weights are summed, the quality weights are summed,and the time weights are summed. For each set of processes, percentageof [sum of cost weights/(sum of cost, quality, and time weights)],percentage of [sum of quality weights/(sum of cost, quality, and timeweights)], and percentage of [sum of time weights/(sum of cost, quality,and time weights)] are calculated. Each percentage is applied to acorresponding process score (e.g., process score 2406 in FIG. 24 ). Forexample, the facilities management process score is 3.4. A costpercentage is multiplied to the facilities management process score toobtain the aggregate cost weight for the facilities management process.A quality percentage is multiplied to the facilities management processscore to obtain the aggregate quality weight for the facilitiesmanagement process. A time percentage is multiplied to the facilitiesmanagement process score to obtain the aggregate time weight for thefacilities management process.

For example, the aggregate performance driver weights for the differentprocess may be represented as follows:

(‘Compliance’, 10, 1.1324, 1.1324, 1.5124)

(‘Procurement’, 17, 1.3859, 1.2212, 1.3741)

(‘Inventory’, 22, 0.5239, 1.0478, 0.6883)

(‘Compliance’, 1, 0.8514, 0.8514, 0.8514)

(‘Inventory’, 20, 1.0491, 0.09369, 1.7503)

(‘OTHER-DOMAIN’, 1, 0.8052, 0.8052, 0.8052)

(‘OTHER-DOMAIN’, 1, 1.0428, 1.0428, 1.0428)

(‘Inventory’, 29, 0.6243, 0.6243, 0.6243)

(‘OTHER-DOMAIN’, 1, 0.6864, 0.6864, 0.6864)

For each set of aggregate performance driver weights for each process,the third to fifth values be aggregate performance driver weights foreach process or each set of processes. Furthermore, the first value maycorrespond to a domain, the second value may correspond to a processidentifier (e.g., process identifiers 2606 in FIG. 26 ), and each valueof the third to fifth value may correspond to a performance driver. Forexample, the third value of each set corresponds to cost, the fourthvalue of each set corresponds to quality, and the fifth value of eachset corresponds to time.

In this example, after aggregate performance driver weights for theprocesses are combined by domain, nine aggregate performance driverweights may be determined. For example, for each of the compliance,procurement, and inventory domains and for each of the cost, quality,and time performance drivers, an average of the aggregate performancedriver weights is calculated to determine the aggregate performancedriver weights associated with the capability process data (e.g., forthe compliance domain, an average of the aggregate cost weights iscalculated to determine the aggregate cost weight associated with thecapability process data; etc.). For example, the aggregate performancedriver weights associated with the capability process data may berepresented as follows:

cap_proc=(1.228, 0.79, 1.012)

cap_inv=(0.89, 0.86, 1.182)

cap_comp=(0.88, 0.95, 1.04)

“cap_proc” may be a set of aggregate performance driver weights for theprocurement domain, “cap_inv” may be a set of aggregate performancedriver weights for the inventory domain, and “cap_comp” may be a set ofaggregate performance driver weights for the compliance domain. For eachset of aggregate performance driver weights, each value corresponds to aperformance driver. For example, the first value of each set correspondsto cost, the second value of each set corresponds to quality, and thethird value of each set corresponds to time.

The aggregate performance driver weights advantageously allows a moreaccurate updated performance score to be determined. The aggregateperformance driver weights reflect emphasis or a de-emphasis of aparticular performance driver based on an occurrence of the particularperformance driver for a domain. For example, if the number ofoccurrence of cost is higher for the inventory domain, the aggregatecost weight would be higher and reflect an emphasis on the costperformance driver, which would provide a more accurate assessment of abusiness in this situation.

The process 2200 may include determining a deviation between aggregateperformance driver weights associated with assessment data and acapability process data (e.g., a gross domain performance driverdeviation) (step 2216). For example, a gross domain performance driverdeviation is determined. The gross domain performance driver deviationmay be represented as follows:

gros_sproc=(0.172, −0.2839, −0.044)

gross_inv=(0.2627, 0.0395, 0.1343)

gross_comp=(−0.374, −0.133, −0.404)

“gros_sproc” may be a set of gross domain performance driver deviationsfor the procurement domain, “gross_inv” may be a set of gross domainperformance driver deviations for the inventory domain, and “gross_comp”may be a set of gross domain performance driver deviations for thecompliance domain. For each set of gross domain performance driverdeviations, each value corresponds to a performance driver. For example,the first value of each set corresponds to cost, the second value ofeach set corresponds to quality, and the third value of each setcorresponds to time.

In the above example, differences between corresponding values inrespective aggregate performance driver weights are determined togenerate the gross domain performance driver deviation values. It isunderstood that the computation in this example is not meant to belimiting, and other methods of generating the gross domain performancedriver deviation may exist.

The process 2200 may include determining a net deviation between theassessment data and the capability process data (e.g., a net domainperformance driver deviation) (step 2218). For example, the net domainperformance driver deviation is determined. The net domain performancedriver deviation may be determined by applying a domain weight to acorresponding to gross domain performance driver deviation. For example,a procurement domain weight is applied (e.g., multiplied) to grossdomain performance driver deviations associated with the procurementdomain (e.g., “gross_proc”), an inventory domain weight is applied(e.g., multiplied) to gross domain performance driver deviationsassociated with the inventory domain (e.g., “gross_inv”), and acompliance domain weight is applied (e.g., multiplied) to gross domainperformance driver deviations associated with the compliance domain(e.g., “gross_comp”).

As an exemplary advantage, using the domain weights, a more unbiasedupdated performance score may be derived. As described herein, thedomain weights may be determined based on questions provided to a userusing the system 100. The questions may be designed to be neural andnon-suggestive, such that the user would not be able to infer aresponse's effect on an updated performance score. That is, the domainweights may be determined objectively using the questions, and the grossdomain performance driver deviations may be appropriately scaled bythese objectively-determined domain weights, which emphasis orde-emphasis deviations based on an objective assessment of the business.

As an example, after the domain weights have been applied to the grossdomain performance driver deviations, the net domain performance driverdeviations may be represented as follows:

net_proc=(0.172, −0.2839, −0.044)

net_inv=(0.2627, 0.0395, 0.1343)

net_comp=(−0.374, −0.133, −0.404)

“net_proc” may be a set of net domain performance driver deviations forthe procurement domain, “net_inv” may be a set of net domain performancedriver deviations for the inventory domain, and “net_comp” may be a setof net domain performance driver deviations for the compliance domain.For each set of net domain performance driver deviations, each valuecorresponds to a performance driver. For example, the first value ofeach set corresponds to cost, the second value of each set correspondsto quality, and the third value of each set corresponds to time.

The process 2200 may include determining an updated performance score(step 2220). The step 2220 may also include determining an overalldeviation. The overall deviation may be a sum of the values of the netdomain performance driver deviations. Although the overall deviation isdescribed as being a sum of the values of the net domain performancedriver deviations, it is understood that this description is not meantto be limiting; other methods of determining an overall deviation mayexist. In the above example, the overall deviation is −0.204.

The updated performance score may be determined based on the overalldeviation. For example, the overall deviation indicates a differencebetween an initial performance score (e.g., determined from step 2104)and an updated performance score, which includes information aboutperformance drivers (e.g., critical factors for business operation) andprocesses and providing a more accurate assessment of a business.

The updated performance score of the target business may be generated byapplying the overall deviation to the initial performance score. Forexample, the initial performance score is added to the overall deviationto generate the updated performance score of the target business. As anexample, if the initial performance score is 3.9 (e.g., from step 2104)and the overall deviation is −0.204, then the updated performance scorewould be rounded to 3.695. In some embodiments, the updated performancescore of the target business and/or overall deviation is displayed orrepresented (e.g., as a bar graph) on a UI, as described herein.

Exemplary KPI Recommendation Process

FIG. 29 illustrates a flowchart of process 2900 for measuring aperformance of the target business, according to embodiments of thisdisclosure. Although the process 2900 is illustrated as including thedescribed elements, it is understood that different order of elements,additional elements, or fewer elements may be included without departingfrom the scope of the disclosure.

As illustrated, the process 2900 includes decomposing a process (step2902). Decomposing a process may include breaking down a larger processof a business into sub-processes using the system 100. In some examples,a larger process may be broken into a five-level hierarchy ofsub-progresses. It is understood that the five-level hierarchy is merelyexemplary; different numbers of levels of process hierarchy may existwithout departing from the scope of the disclosure. Decomposing aprocess may also include providing processes associated with a targetbusiness (e.g., in the system 100). In some examples, scores (e.g.,experience, efficiency, data quality, automation) associated with eachof the process and information (e.g., owner of the process, location ofthe process, priority of the process) associated with each of theprocess may be additionally provided. In some examples, the processesand the information associated with the processes are at least in partprovided by someone in the company associated with the process (e.g., amanager, a person whose job description is associated with the process).In some examples, the provided processes are determined at least in partbased on industry standards.

The step of decomposing a process may be associated with processdecomposition 2 in FIG. 1 . With process decomposition 2, the processesmay be organized in a Functional Phase Matrix (FPM). For example, theFPM includes functions of a target company and phases. The FPM functionsmay include application, capital management, operations, businesstransformation, inventory planning, demand planning, procurement,information technology, and sales management. The FPM phases may includestrategy and planning, monitor and management, and execution.

The process 2900 may include processing provided data (step 2904). Thedecomposed processes from step 2902 may be processed by the system 100.For example, the decomposed processes are saved in a database of thesystem 100. The decomposed processed may be used to assess a performanceof a business (e.g., determine an updated performance score, asdescribed with respect to FIG. 22 ; identify pain-points, as describedwith respect to step 2906).

The process 2900 may include identifying pain-points (step 2906). Forexample, a pain-point may be an inefficient process or a weakness in aproduct. Pain-points may be issues, obstacles, or creators of poorperformance in a process that prevent optimal process execution andoptimal results. The pain-points may be identified based on the processdata from step 2902 and/or step 2904. For example, using an artificialintelligence (AI) algorithm, NLP (e.g., semantic match), or machinelearning algorithm, pain-points are identified from the process data.Using an AI algorithm, NLP (e.g., semantic match), or machine learningalgorithm, pain-points may be more accurately identified (e.g.,pain-points may be less likely missed, compared to manualidentification; pain-point identification may be more objective). Asanother example, the pain-points are identified based on providedinformation associated with the processes (e.g., experience score,efficiency score, data quality score, and automation score). Theidentified pain-points may be used to determine a recommendation, asdescribed in more detail with respect to process 3000.

The process 2900 may include generating a roadmap (step 2908). Based onthe identified pain-points (e.g., form step 2906), a roadmap may begenerated (e.g., in roadmap 5 of FIG. 1 ). The roadmap may track theidentified pain-points and include actions that may be required toeliminate the pain-points. The roadmap may include a plan or a schedulefor a business to follow in order to eliminate the pain-points. Theroadmap may be tracked over time to determine a business' progress ofeliminating a pain-point or resolving the problem.

The process 2900 may include measuring a performance (step 2910). Forexample, the performance may be measured in KPIs. The KPIs may beprovided based on a recommendation provided for a problem and/orpain-points (e.g., from process 3000). The determination of therecommendation is described in more detail with respect to process 3000.In some embodiments, the KPI are measured, set, and/or tracked in thesystem 100.

FIG. 30 illustrates a flowchart of process 3000 for determining anupdated performance score of the target business, according toembodiments of this disclosure. Although the process 3000 is illustratedas including the described elements, it is understood that differentorder of elements, additional elements, or fewer elements may beincluded without departing from the scope of the disclosure.

As illustrated, the process 3000 includes providing a problem data (step3002). The problem data may include information about problems that abusiness may experience or problems that a business may want to resolve.The information of the problem data may be organized by process,function, and or phase. Information of the problem data may be stored inthe second database 207 and may be retrieved by a database of the system100.

In some examples, the information of the problem data is inputted to afile to create the problem data. In some examples, the information ofthe problem data is provided a UI (e.g., a UI of the system 100) tocreate the problem data. For example, the UI may prompt a user toprovide information to create the problem data (e.g., problems, processtitle, function, and phase). As another example, the UI may prompt theuser to provide problems that a business is trying to solve andprocesses related to the problems being solved. In some examples, theinformation of the problem data is at least in part automaticallygenerated. For example, the information of the problem data is generatedbased on an assessment of a business and potential problems identifiedin the assessment. As another example, some information of the problemdata is generated (e.g., by system 100) in response to problem datainformation provided by a user. The problem data may include a list ofproblems, which may be obstacles and poor performing aspects of a targetbusiness discovered while assessing processes of the target business.

FIG. 31 illustrates exemplary problem data 3100, according toembodiments of this disclosure. As illustrated, the problem data 3100includes problems 3102, process titles 3104, functions 3106, and phase3108. As discussed with respect to step 3002, the information associatedwith the problem data 3100 may be provided by a user and/or generatedautomatically.

The problems 3102 may be problems that a business may experience andwould like to resolve. The process titles 3104 may be processescorresponding to the problems 3102. For example, the problem “Purchaseof bad quality raw materials” may correspond to the process “ProcurementQuality Management.” In some examples, the corresponding process isprovided by a user (e.g., a process owner) to the system 100. In someexamples, the corresponding process is determined by the system 100based on the problems 3102. For example, AI or NLP (e.g., semanticmatch) is used to determine a process corresponding to a problem.Although the process titles 3104 are illustrated as being textual, it isunderstood that the process titles may be represented in other forms,such as numbers or codes.

The functions 3106 may be function areas corresponding to the processes3104. The functions may be FPM functions described with respect toprocess decomposition 2. For example, the process “Procurement QualityManagement” may correspond to the function area “Inventory Planning.” Insome examples, the corresponding function is provided by a user (e.g., aprocess owner) to the system 100. In some examples, the correspondingfunction is determined by the system 100 based on the process 3102. Forexample, a function is determined to correspond to a process based oninformation about the business' processes (e.g., stored in the system100). As another example, an AI or machine learning algorithm is used todetermine a function corresponding to a process. Although the functions3106 are illustrated as being textual, it is understood that thefunctions may be represented in other forms, such as numbers or codes.

The phases 3108 may be phases (e.g., a point in progress of acorresponding process) corresponding to the processes 3104. The phasesmay be FPM phases described with respect to process decomposition 2. Forexample, the process “Procurement Quality Management” may correspond tothe phase “Strategy and Planning.” That is, the problem process“Procurement Quality Management” is indicated to be in the “Strategy andPlanning” phase. In some examples, the corresponding phase is providedby a user (e.g., a process owner) to the system 100. In some examples,the corresponding phase is determined by the system 100 based on theprocess 3102. For example, a phase is determined to correspond to aprocess based on information (e.g., a schedule of the processes) aboutthe business' processes (e.g., stored in the system 100). As anotherexample, an AI or machine learning algorithm is used to determine aphase corresponding to a process. Although the phases 3108 areillustrated as being textual, it is understood that the phases may berepresented in other forms, such as numbers or codes.

Although the problem data 3100 is illustrated as being organized incolumn and rows and including the described information, it isunderstood that the illustration of the spreadsheet 3100 is merelyexemplary. It is understood that the spreadsheet 3100 may be representedin other forms, may be organized in a different manner, and/or mayinclude different information without departing from the scope of thedisclosure.

Returning to FIG. 30 , the process 3000 includes providing a pain-pointand solution data (step 3004). The pain-point and solution data mayinclude information about pain-points a business may experience andsolutions that may help the business alleviate these pain-points. Forexample, the information is organized by pain-points, function, rootcause, and solutions. For example, for a health science business, theinformation may include the business' supply chain issues, theirassociated functional areas, root causes, and solutions. In someembodiments, the pain-points are determined based on processes of abusiness (e.g., identified at step 2906). In some embodiments, thepain-points are provided by a user (e.g., a manager of a product) to thesystem 100. For example, a pain-point may be an inefficient process or aweakness in a product.

The pain-point and solution data may be stored in a database and may beconfigured to be updated. For example, the pain-point and solution dataincludes knowledge and inputs from SME and the knowledge and inputs areupdated over time, as more information is being provided to thepain-point and solution data. Based on the SME's expertise, he or shemay provide pain-points that may be relevant to a business and solutionsthat would help a business alleviate these pain-points. The pain-pointsand possible solutions may be advantageously consolidated in thepain-points and solutions data. As described in more detail herein, thepain-point and solution data may be updated based on an effectiveness ofa proposed pain-point or solution KPI to a problem.

The SME's knowledge and input may be processed by AI or machine learningalgorithms to advantageously increase the value of the knowledge and theinput. For example, AI or machine learning algorithms may expand theapplicability of the knowledge and input from the SME. As an example, aninput from a SME is indicated to be applicable to a first industry or afirst process. Using AI or machine learning algorithm, the input may bedetermined to be applicable to a second industry or a second process,and the pain-point and solution data may be updated accordingly toexpand the knowledge and input's applicability beyond the initial scopeof first industry or first process, increasing the value of the providedknowledge.

As another example, using AI or machine learning algorithms, a secondinput from a same SME or a different SME may affect existing informationin the pain-point and solution data. For example, a SME provides asecond input to the pain-point and solution data, and using AI ormachine learning algorithms, it is determined that a first input shouldbe updated (e.g., for improved accuracy, for improved efficiency, toprovide additional information) in response to receiving the secondinput. In accordance with the determination that the first input shouldbe updated, information associated with the first input is updated basedon the second information.

As an exemplary advantage, using the pain-point and solution data toresolve problems for a business reduces costs for the business andprovides a business with an up-to-date set of recommendations to aproblem. For example, instead of hiring a consultant to resolve abusiness' problems, the system 100 and/or the process 3000 allows abusiness to take advantage of the knowledge and inputs from manyexperts, and because the knowledge and inputs of these experts may beshared between multiple users, a cost associated with access to thisinformation would be reduced accordingly. Additionally, the pain-pointand solution data may be configured to be updated in real-time (e.g., toinclude the latest, most applicable, and most accurate information),meaning a business seeking a recommendation would receive the latest andbest recommendation from the system 100.

FIG. 32 illustrates an exemplary pain-point and solution data 3200,according to embodiments of this disclosure. As illustrated, thepain-point and solution data 3200 includes pain-points 3202, functions3204, root causes 3206, and solutions 3208. As described with respect tostep 3004, the information associated with the pain-point and solutiondata 3200 may be based on input provided by a SME, updated using AI ormachine learning algorithms, and/or updated based on effectiveness of arecommendation. In some embodiments, the pain-points are determinedbased on processes of a business (e.g., identified at step 2906). Insome embodiments, the pain-points are provided by a user (e.g., amanager of a product) to the system 100.

The pain-points 3202 may be pain-points potentially experienced by abusiness. As illustrated, a pain-point may be “Manual procurement istedious.” The functions 3004 may be functions associated with thepain-points. More than one function may correspond to a pain-point. Asillustrated, the pain-point “Manual procurement is tedious” may havecorresponding functions “procurement” and “information technology.” Insome examples, the functions are provided by a SME. In some examples, anAI or machine learning algorithm is used to determine functionscorresponding to a pain-point. In some examples, the functionscorresponding to a pain-point are determined based on information (e.g.,about functions) stored in the system 100. Although the pain-points 3202and functions 3204 are illustrated as being textual, it is understoodthat the pain-points and functions may be represented in other forms,such as numbers or codes.

The root causes 3206 may be root causes corresponding to functions 3204.As illustrated, the pain-point “Manual procurement is tedious” and thecorresponding function “procurement” have a root cause of “Lack ofclearly defined approval hierarchy for product categories.” In someexamples, the root causes are provided by a SME. In some examples, theroot causes may be provided or updated using AI or machine learningalgorithm (e.g., for improved accuracy, for improved efficiency). Insome examples, the root causes may be determined based on an assessmentof a business (e.g., using system 100). Although the root causes 3206are illustrated as being textual, it is understood that the root causesmay be represented in other forms, such as numbers or codes.

The solutions 3208 may be solutions corresponding to a pain-point 3202and/or function 3206. The solutions 3208 may also be associated with theroot cause 3206. For example, the solutions 3208 may be ways toeliminate the root causes 3206. As illustrated, the pain-point “Manualprocurement is tedious” and the corresponding function “procurement”have a root cause of “Lack of clearly defined approval hierarchy forproduct categories and a solution of “Approval hierarchy need to beclearly defined by product categories as well as value of the purchaseorder.” In some examples, the solutions are provided by a SME. In someexamples, the solutions may be provided or updated using AI or machinelearning algorithm (e.g., for improved accuracy, for improvedefficiency). In some examples, the solutions may be determined based onan assessment of a business (e.g., using system 100). Although thesolutions 3208 are illustrated as being textual, it is understood thatthe solutions may be represented in other forms, such as numbers orcodes.

Although the pain-point and solution data 3200 is illustrated as beingorganized in column and rows and including the described information, itis understood that the illustration of the pain-point and solution data3200 is merely exemplary. It is understood that the pain-point andsolution data 3200 may be represented in other forms, may be organizedin a different manner, and/or may include different information withoutdeparting from the scope of the disclosure.

Returning to FIG. 30 , the process 3000 may include comparing theproblem data and the pain-point and solution data (step 3006). Theproblem data and the pain-point and solution data may be compared usingAI algorithms or NLP. For example, NLP's Spacy library or semantic matchis used to compare between the two data. In some examples, semanticsimilarity scores is calculated based on the comparison.

For example, if an element of the problem data is more similar to anelement of the pain-point and solution data, a semantic similar scorefor these elements may be higher, and if an element of the problem datais less similar to an element of the pain-point and solution data, asemantic similar score for these elements may be lower. As an example, aproblem of the problem data (e.g., problems 3102) may be highly similarto a pain-point of the pain-point and solution data (e.g., pain-points3202) when the elements between the data include highly substantialsimilarities; the pain-point may likely be the pain-point correspondingto the problem.

In some embodiments, all the problems of the problem data are comparedwith the pain-points of the pain-point and solution data until at leasta semantic similarity score is given for a pain-point for each of theproblems. For example, the problems of the problem data are comparedwith the pain-points of the pain-point and solution data until a mostlikely corresponding pain-point is determined for each problem.

In some examples, a problem may not have a corresponding pain-point ormay have a corresponding pain-point with a low semantic similarity score(e.g., below a semantic similarity score threshold). In these instances,a user may be notified that an acceptable pain-point match for a problemhas not been found. A SME may review the problem, provide acorresponding pain-point, and provide a recommendation to resolve theproblem. In some embodiments, the provided corresponding pain-point andrecommendation may be added to the pain-point and solution database.When a user provides a problem data including this previously-unmatchedproblem, this problem may now be matched with this newly addedpain-point and solution.

As an exemplary advantage, by using AI algorithm or NLP to match theproblem data and the pain-point and solution data, a more suitable matchbetween a problem and a pain-point may be automatically and moreefficiently determine. For example, a problem does not need to bemanually matched with a pain-point from a large pain-point and solutiondatabase. Not only the manually matching process may be tedious, it mayalso not lead to the most suitable match; the wealth of knowledge andsolutions from the pain-point and solution database may not beefficiently utilized. When a more suitable pain-point is matched with aproblem (e.g., using AI algorithm or NLP), a more suitablerecommendation would be given to address the problem, compared to apain-point that is manually matched with a problem.

The process 3000 may include identifying a matching pain-point (step3008). A pain-point from the pain-point and solution data may be matchedwith a problem of the problem data based on a semantic similarity score.For example, from step 3006, one or more pain-points potentially matchedwith the problem are given semantic similarity scores. In some examples,the pain-point with the highest semantic similarity score is identifiedas the matching pain-point to the problem. In some examples, more thanone pain-points with the highest semantic similarity scores areidentified as matching pain-points to the problem (e.g., more than onerecommendation may be made for the problem). In some examples, apain-point above a semantic similar score threshold is identified as thematching pain-point to the problem.

In some examples, a matching pain-point may not be identified for aproblem because the problem may not have a matching pain-point or mayhave a corresponding pain-point with a low semantic similarity score(e.g., below a semantic similarity score threshold). In these instances,a user may be notified that an acceptable pain-point match for a problemhas not been found. A SME may review the problem, provide acorresponding pain-point, and provide a recommendation to resolve theproblem. In some embodiments, the provided corresponding pain-point andrecommendation may be added to the pain-point and solution database.When a user provides a problem data including this previously-unmatchedproblem, this problem may now be matched with this newly addedpain-point and solution.

The process 3000 may include identifying a function (step 3010). Afterthe problems from the problem data are matched with pain-points from thepain-point and solution data (e.g., from step 3008), functionscorresponding to the pain-points may be identified.

For example, the function is function 3204, and each matched pain-pointmay have one or more corresponding functions. As an example, asdescribed with respect to FIG. 32 , a matched pain-point to a problemmay be “Manual procurement is tedious.” The pain-point “Manualprocurement is tedious” may have corresponding functions “procurement”and “information technology.” Accordingly, these corresponding functionsmay be identified based on the pain-point. In some examples, thefunctions are identified from the pain-point and solution data (e.g.,provided by a SME, provided using an AI or machine learning algorithm,provided using system 100), and a corresponding function may beidentified from the pain-point and solution data based on a pain-point.In some examples, an AI or machine learning algorithm is used toidentify functions corresponding to a pain-point. In some examples, thefunctions corresponding to a pain-point are identified based oninformation (e.g., about functions) stored in the system 100.

The process 3000 may include identifying a root cause (step 3012). Forexample, after functions corresponding to the pain-points are identified(e.g., from step 3010), root causes corresponding to the functions areidentified. As another example, the root cause are identified after theproblems from the problem data are matched with pain-points from thepain-point and solution data (e.g., from step 3008).

For example, the root cause is root cause 3206, and a root cause maycorrespond to an identified function (e.g., from step 3010) or to amatched pain-point (e.g., from step 3008). For example, as describedwith respect to FIG. 32 , for a matched pain-point “Manual procurementis tedious” and/or an identified function “procurement,” a root cause of“Lack of clearly defined approval hierarchy for product categories” maybe identified. In some examples, the root causes are identified from thepain-point and solution data (e.g., provided by a SME, provided using anAI or machine learning algorithm, provided using system 100), and acorresponding root cause may be identified from the pain-point andsolution data based on an identified function or a matched pain-point.In some examples, an AI or machine learning algorithm is used toidentify a root cause corresponding to an identified function or amatched pain-point. In some examples, the root cause corresponding to anidentified function or a matched pain-point are identified based oninformation (e.g., about functions) stored in the system 100.

The process 3000 may include identifying a solution (step 3014). Forexample, after root causes are identified (e.g., from step 3012),solutions corresponding to the root causes are identified. As anotherexample, the solutions are identified after the problems from theproblem data are matched with pain-points from the pain-point andsolution data (e.g., from step 3008) or after functions corresponding tothe pain-points are identified (e.g., from step 3010). In someembodiments, the identified solutions are published in the system 100.

For example, the solution is solution 3208, and a solution maycorrespond to an identified root cause (e.g., from step 3012), to anidentified function (e.g., from step 3010), or to a matched pain-point(e.g., from step 3008). For example, as described with respect to FIG.32 , for a matched pain-point “Manual procurement is tedious,” anidentified function “procurement,” and/or an identified root cause of“Lack of clearly defined approval hierarchy for product categories,” asolution of “Approval hierarchy need to be clearly defined by productcategories as well as value of the purchase order” is identified. Insome examples, the solutions are identified from the pain-point andsolution data (e.g., provided by a SME, provided using an AI or machinelearning algorithm, provided using system 100), and a correspondingsolution may be identified from the pain-point and solution data basedon an identified root cause, function, or a matched pain-point. In someexamples, an AI or machine learning algorithm is used to identify asolution corresponding to an identified root cause, an identifiedfunction, or a matched pain-point. In some examples, the solutioncorresponding to an identified root cause, an identified function, or amatched pain-point are identified based on information (e.g., aboutfunctions) stored in the system 100.

The process 3000 may include creating initiatives (step 3016). In someembodiments, the initiatives are created in the system 100 (e.g., inroadmap 5 of FIG. 1 ). The initiatives may be created based on theidentified solutions. The initiatives may be actions that may berequired to rectify the problems from the problem data. The initiativesmay form a plan or a schedule (e.g., in the system 100) for a businessto follow in order to resolve the problems from the problem data. Theinitiatives may be tracked over time (e.g., in the system 100) todetermine a business' progress of resolving the problem.

The process 3000 may include identifying an associated solution KPI(step 3018). For example, an identified solution (e.g., from step 3014)may have an associated solution KPI. Even though step 3018 isillustrated as following step 3016, it is understood that step 3018 maybe performed at a different order than illustrated. For example, step3018 may be performed after a solution is identified (e.g., step 3014).

The process 3000 may include in response to identifying an associatedsolution KPI, recommending a solution KPI (step 3020). The solution KPIrecommendation may be provided as an input to the system 100. Thesolution KPI may be used to monitor performance improvement (e.g., aprogress associated with resolution of a problem), allowing a businessto advantageously track improvements or regressions (e.g., at solving aproblem) in a simple and quantitative manner.

Low performing or error-prone processes or sub-process and underlyingcauses may be advantageously monitored using KPIs (e.g., solution KPI,pain-point KPI). For example, the KPIs may provide details of metricsfor an outcome-based approach. The KPIs may associate business outcomeswith necessary capabilities and potential risk to project success rate.

In some examples, an associated solution KPI may not be identified(e.g., there may not be a solution KPI corresponding to the solution),recommendation of a solution KPI is forgone. In some examples, inresponse to not identifying an associated solution KPI, a correspondingsolution KPI recommendation may be added (e.g., by a SME, using AI ormachine learning algorithms).

FIG. 33 illustrates exemplary solution KPI 3300, according toembodiments of this disclosure. As illustrated, the solution KPI 3300includes solutions 3302 and solution KPI recommendations 3304. Thesolutions 3302 may be solutions identified from step 3014. A solutionKPI corresponding to an identified solution may be provided by a SME(e.g., to the system 100, to the pain-point and solution data, to asolution KPI database). In some examples, an AI or machine learningalgorithm is used to determine a solution KPI corresponding to anidentified solution. In some examples, a solution KPI corresponding toan identified solution is determined based on information stored in thesystem 100.

For example, as illustrated, for a solution “Approval hierarchy need tobe clearly defined by product categories as well as value of thepurchase order,” a solution KPI recommendation of “Inventory Management”is identified. Some solutions may not have a corresponding solution KPIrecommendation. For example, for a solution “Create robust IT systemintegration across product categories,” a solution KPI recommendationwould not be identified. In some examples, if a solution does not have acorresponding solution KPI, a corresponding solution KPI recommendationmay be added (e.g., by a SME, using AI or machine learning algorithms).Although the solutions 3302 and solution KPI recommendations 3304 areillustrated as being textual, it is understood that the solutions andsolution KPI recommendations may be represented in other forms, such asnumbers or codes.

Although the solution KPI 3300 is illustrated as being organized incolumn and rows and including the described information, it isunderstood that the illustration of the solution KPI 3300 is merelyexemplary. It is understood that the solution KPI 3300 may berepresented in other forms, may be organized in a different manner,and/or may include different information without departing from thescope of the disclosure.

Returning to FIG. 30 , the process 3000 may include identifying anassociated pain-point KPI (step 3022). For example, a matched pain-point(e.g., from step 3008) may have an associated pain-point KPI. Eventhough step 3022 is illustrated as following step 3016, it is understoodthat step 3022 may be performed at a different order than illustrated.For example, step 3022 may be performed after a matching pain-point isidentified (e.g., step 3008).

The process 3000 may include in response to identifying an associatedpain-point KPI, recommending a pain-point KPI (step 3020). Thepain-point KPI recommendation may be provided as an input to the system100. The pain-point KPI may be used to monitor performance improvement(e.g., a progress associated with elimination of a pain-point), allowinga business to advantageously track improvements or regressions (e.g., ateliminating a pain-point) in a simple and quantitative manner. In someexamples, an associated pain-point KPI may not be identified (e.g.,there may not be a pain-point KPI corresponding to the pain-point),recommendation of a pain-point KPI is forgone. In some examples, inresponse to not identifying an associated pain-point KPI, acorresponding pain-point KPI recommendation may be added (e.g., by aSME, using AI or machine learning algorithms).

FIG. 34 illustrates exemplary pain-point KPI 3400, according toembodiments of this disclosure. As illustrated, the pain-point KPI 3400includes pain-points 3402 and pain-point KPI recommendations 3404. Thepain-points 3402 (e.g., matched pain-points) may be solutions identifiedfrom step 3008. A pain-point KPI corresponding to a matched pain-pointmay be provided by a SME (e.g., to the system 100, to the pain-point andsolution data, to a pain-point KPI database). In some examples, an AI ormachine learning algorithm is used to determine a pain-point KPIcorresponding to a matched pain-point. In some examples, a pain-pointKPI corresponding to a matched pain-point is determined based oninformation stored in the system 100.

For example, as illustrated, for a pain-point “Manual procurementprocess is tedious,” a pain-point KPI recommendation of “Purchase OrderCycle Time” is identified. In some examples, some matched pain-pointsmay not have a corresponding pain-point KPI recommendation (not shown).In some examples, if a pain-point does not have a correspondingpain-point KPI, a corresponding pain-point KPI recommendation may beadded (e.g., by a SME, using AI or machine learning algorithms).Although the solutions 3302 and solution KPI recommendations 3304 areillustrated as being textual, it is understood that the solutions andsolution KPI recommendations may be represented in other forms, such asnumbers or codes.

Although the pain-point KPI 3400 is illustrated as being organized incolumn and rows and including the described information, it isunderstood that the illustration of the pain-point KPI 3400 is merelyexemplary. It is understood that the pain-point KPI 3400 may berepresented in other forms, may be organized in a different manner,and/or may include different information without departing from thescope of the disclosure.

Returning to FIG. 30 , the process 3000 may include summarizing arecommendation (step 3026). For example, summarizing a recommendationincludes saving the identified solutions (e.g., from step 3014),recommended solution KPI (e.g., from step 3020), and/or recommendedpain-point KPI (e.g., from step 3024) in one or more files (e.g., arecommendation file, recommendation file 3500). The one or more filesmay be used for further processing (e.g., for a user to review, trackinga progress of resolving a problem, tracking a progress of eliminating apain-point, assessing a performance of a business) to improveperformance of the business.

FIG. 35 illustrates exemplary recommendation file 3500, according toembodiments of this disclosure. As illustrated, the recommendation file3500 includes problems 3502, functions 3504, root causes 3506, solutions3508, solution KPI recommendations 3510, and pain-point KPIrecommendations 3512. In some embodiments, the recommendation file 3500is configured to be filtered. For example, the recommendation file 3500is configured to show a subset of all of recommendations in response toa user input (e.g., a user requested to see recommendations associatedwith certain domains).

For example, the problems 3502 are problems 3102 (e.g., identified atstep 3002, step 3006, and/or step 3008), functions 3504 are functions3204 (e.g., identified at step 3010), root causes 3506 are root causes3206 (e.g., identified at step 3012), solutions 3508 are solutions 3208(e.g., identified at step 3014), solution KPI recommendations 3510 aresolution KPI recommendations 3304 (e.g., identified at step 3020), andpain-point KPI recommendations 3512 are pain-point recommendations 3404(e.g., identified at step 3024). The recommendation file 3500 mayadvantageously provide a comprehensive summary of a business' morecritical problems, solutions to the problem, and quantitative metrics(e.g., KPIs) to monitor and track progress of the solutions.

Although the recommendation file 3500 is illustrated as being organizedin column and rows and including the described information, it isunderstood that the illustration of the recommendation 3500 is merelyexemplary. It is understood that the recommendation 3500 may berepresented in other forms, may be organized in a different manner,and/or may include different information without departing from thescope of the disclosure. For example, the recommendation 3500 mayinclude matched pain-points (e.g., from step 3008).

Returning to FIG. 30 , as another example, summarizing a recommendationincludes presenting the identified solutions (e.g., from step 3014),recommended solution KPI (e.g., from step 3020), and/or recommendedpain-point KPI (e.g., from step 3024) on a UI. The UI may be a dashboardof the system 100. The UI may allow the recommendation to be moreeffectively summarized and more critical information from therecommendation may be identified to effectively improve performance of abusiness.

FIG. 36 illustrates an exemplary UI 3600, according to embodiments ofthis disclosure. The UI 3600 may be a dashboard of the system 100. Asillustrated, the UI 3600 includes pain-points 3602, functions 3604,solutions 3606, KPI recommendations 3608, and UI object 3610. In someembodiments, the UI 3600 is configurable. For example, a user mayprovide an input to the UI 3600 to sort, filter, hide, or manipulate theinformation displayed on the UI.

For example, the pain-points 3602 are pain-points 3202 (e.g., identifiedat step 3008), functions 3604 are functions 3204 (e.g., identified atstep 3010), solutions 3606 are solutions 3208 (e.g., identified at step3014), and KPI recommendations 3608 include solution KPI recommendations3304 (e.g., identified at step 3020) and/or pain-point recommendations3404 (e.g., identified at step 3024). The UI 3610 may include arepresentation (e.g., a visual representation, a pie chart, a bar graph,a plot) of information associated with a recommendation. For example, asillustrated, the UI 3610 is a pie chart summarizing numbers of solutionsin the different function areas. It is understand that the illustratedinformation and the illustrated representation for UI 3610 is not meantto be limiting. The UI 3600 may advantageously provide a pointed summaryof a business' more critical problems, solutions to the problem, andquantitative metrics (e.g., KPIs) to monitor and track progress of thesolutions.

Although the UI 3600 is described as illustrated, it is understood thatthe illustration of the UI 3600 is merely exemplary. It is understoodthat the UI 3600 may be presented in other forms, may be organized in adifferent manner, and/or may include different information withoutdeparting from the scope of the disclosure. For example, the UI 3600 mayinclude problems (e.g., identified at step 3002, step 3006, and/or step3008) and/or root causes (e.g., identified at step 3012).

The process 3000 may include updating the pain-point and solution data(not shown in FIG. 30 ). The pain-point and solution data may be updatedin accordance with an effectiveness of a recommendation. For example, ifa recommended solution and/or KPI is determined to be effective (e.g.,the recommendation helps a business resolve a problem and/or eliminate apain-point), then the pain-point and solution data is updated to morelikely recommend the same recommendation for a corresponding problem(e.g., a problem and pain-point corresponding to the effectiverecommendation are more likely matched). As another example, if arecommended solution and/or KPI is determined to be not effective (e.g.,the recommendation does not help a business resolve a problem and/oreliminate a pain-point), then the pain-point and solution data isupdated to less likely recommend the same recommendation for acorresponding problem (e.g., a problem and pain-point corresponding tothe effective recommendation are less likely matched).

For example, the system 100 determines whether or not an effectivenessof the solution to the problem (e.g., a quantitative indicator of aneffectiveness of the solution to the problem) is below an effectivenessthreshold (e.g., a minimum requirement of the quantitative indicator).When the effectiveness of the solution to the problem is below theeffectiveness threshold, the system 100 disassociates the solution withthe problem. When the effectiveness of the solution to the problem isnot below the effectiveness threshold, the system 100 forgoesdisassociating the solution with the problem. By updating the pain-pointand solution data in accordance with an effectiveness of arecommendation, the pain-point and solution data may be further improved(e.g., additional improvement to SME contributions to the pain-point andsolution data over time) over time to account for effectiveness of therecommendations.

Exemplary Design Thinking Process

In some embodiments, the system 100 may be configured to provide aplatform to enable stakeholders, (e.g., users) in the target business toperform a design thinking process. The design thinking process canprovide stakeholders with a structure for identifying and solvingcomplex problems to innovate within the target business. In someembodiments, the system 100 can provide users with a platform thatenables them to address multiple problems simultaneously through thedesign thinking process. In some embodiments, the design thinkingprocess can conclude with an adaptive roadmap. In some embodiments, thesystem 100 can provide a design thinking platform that can leverage datagathered from other processes performed by the system, e.g.,benchmarking processes, capability process, KPI process, etc.

Embodiments of the design thinking process in accordance with thisdisclosure may integrate the design thinking process with otherprocesses disclosed herein, including, for example, benchmarking,process decomposition, and performance measuring. For example, thedesign thinking process can apply outcomes and data determined duringthese other processes to aid in decision making throughout the designthinking process.

FIG. 37 illustrates a flowchart of process 3700 for performing a designthinking (DT) process, according to embodiments of this disclosure.Although the process 3700 is illustrated as including the describedelements, it is understood that different order of elements, additionalelements, or fewer elements may be included without departing from thescope of the disclosure. For example, in some embodiments, the process3700 may be a non-linear process.

As illustrated, the process 3700 may include framing the problem (step3701). Framing the problem may include receiving one or more problemstatements from a certain user, referred to as a DT coordinator (e.g.,DT champion). The step 3701 may include refining the one or more problemstatements using data gathered during one or more of the processesdescribed above. Additionally or alternatively, the problem statement(s)may be refined based on one or more sub-problem statements. The DTcoordinator may be an individual responsible for completing the DTprocess in collaboration with identified stakeholders to resolve the oneor more problem statements.

The process 3700 may include empathizing with stakeholders (step 3703).During step 3703, the system 100 can identify stakeholders affected bythe one or more problem statements, receive empathy data related to theidentified stakeholders, build a customer journey map for eachstakeholder (e.g., as a persona), and aggregate the customer journeymaps to build an aggregated problem statement experience map. In someembodiments, the system 100 can rely on data gathered during otherprocesses to identify additional stakeholders and processes impacted bythe problem statement (e.g., in the influence mapped discussed ingreater detail below).

In step 3705, the system 100 can provide the identified stakeholders aplatform to brainstorm and collaborate to identify potential solutionsto the one or more problem statements. In some embodiments, the system100 may enable stakeholders located around the globe to engage in thecollaborative brainstorming process. The system 100 may save data foreach brainstorming session to the second database 207.

In some embodiments, the system may recommend one or more potentialsolutions. In some embodiments, the DT coordinator can select one ormore potential solutions to be prototyped in step 3707. During step3707, the system 100 can provide a platform for prototyping the selectedsolution. Each iteration of the prototyping process can be saved as aseparate, retrievable version in the second database.

The DT process 3700 may also include building a roadmap (step 3709). Theroadmap may include one or more milestones to be completed according toa predetermined schedule. In some embodiments, the system 100 cangenerate the roadmap and set the schedule based on the informationprovided in the earlier steps of the DT process 3700. In someembodiments, the roadmap may include probabilities of the likelihood ofcompleting each milestone based on the schedule.

FIG. 38 illustrates a flowchart of process 3701 for framing the problem.Although the process 3701 is illustrated as including the describedelements, it is understood that different order of elements, additionalelements, or fewer elements may be included without departing from thescope of the disclosure.

As illustrated, the process 3701 may include receiving an initialproblem statement (step 3801). In some embodiments the system 100 canreceive the initial problem statement from the DT coordinator via thewebsite 201. For example, the DT coordinator may input their conceptionof the initial problem statement into a UI provided via website 201. Insome embodiments, the DT coordinator can enter one or more initialproblem statements. Each initial problem statement may be broken downinto one or more sub-problem statements. In some embodiments, receivingthe initial problem statement may include adopting (described in moredetail below) and saving the initial problem statement to the seconddatabase 207.

In step 3803, the system 100 can extract data related to the targetbusiness from the second data base 207. For example, the data related tothe target business may include data gathered from other processes ofperformed by the system, e.g., benchmarking processes, processdecomposition, KPI setting. The system 100 may access this data from thesecond database 207. In step 3805, the system 100 may generate updatedproblem statements based on the data retrieved in step 3803. In someembodiments, the updated problem statements can include one or moreupdated sub-problem statements.

In step 3807, the updated problem statement can be saved to the seconddatabase 207. In some embodiments, saving the updated problem statementmay include saving one or more updated sub-problem statements to thesecond database 207. In this way, the system 100 can create a virtualpaper trail of each version of the initial and updated problemstatements.

At step 3809, the system 100 can adopt the updated problem statement.The adopted updated problem statement may be used going forward throughthe remainder of the DT process as described below. In some embodiments,the system 100 may receive an indication that a user (e.g., the DTcoordinator) approves the updated problem statement, and the system 100can adopt the approved-updated problem statement. In such embodiments,if the user does not approve the updated problem statement, the system100 may use the most-recently adopted version of the problem statement.In some embodiments, the system 100 may automatically use thesystem-updated problem statement going forward through the remainder ofthe DT process without user input.

In some embodiments, the user can edit the system-updated problemstatement with a user-updated problem statement (step 3811). In suchembodiments, following receipt of the user-updated problem statement,the system 100 can repeat steps 3803-3809, as described above. Asdescribed above, the DT process 3700 can be a nonlinear process. In someembodiments, once the user has started other processes of the DT process3700 (e.g., empathizing step 3703, brainstorming step 3705, etc.) theuser may update the problem statement.

FIG. 39 illustrates an exemplary problem statement UI 3900. Asillustrated, the problem statement details 3910 may include an adoptedproblem statements 3912, goal 3914, description 3916, and network ofinfluence 3918 (described in more detail below). The problem statement3912 may reflect the adopted problem statement. In some embodiments, theuser may provide the system 100 with the goal(s) and description(s). Insome embodiments, the system may generate the goal(s) anddescription(s). In some embodiments, the system may update user-providedgoal(s) and description(s). In some embodiments, the problem statementUI may present one or more projects related to other processes performedby the system 100 for the target business. For example, as illustrated,the problem statement UI 3900 presents one or more benchmarking projects3922, capability modeling projects, 3924, and KPIs 3926. In someembodiments, the system 100 may automatically populate the one or morebenchmarking projects 3922, capability modeling projects 3924, and KPIs3926, based on user inputs provided in the other processes describedabove. The projects may be related to the problem statement. Asillustrated in the FIG. the one or more benchmarking projects 3922,capability modeling projects 3924, and KPIs 3926 may includecorresponding scores. The scores may be based on benchmarking scoresdetermined in the benchmarking and capability modeling processes.

FIG. 40 illustrates a flowchart of process 3703 for empathizing with astakeholder. Although the process 3703 is illustrated as including thedescribed elements, it is understood that different order of elements,additional elements, or fewer elements may be included without departingfrom the scope of the disclosure.

As illustrated, the process 3703 includes identifying stakeholders. Insome embodiments, the system 100 can present a user (e.g., the DTcoordinator), via website 201, an influence map template correspondingto the adopted problem statement. The system 100 can identifystakeholders via an initial influence map completed by the user (step4001). The initial influence map may be saved to the second database207. An influence map may include a list of stakeholders of the targetbusiness that are affected by the adopted problem statement. As referredto herein, an impactee may be a stakeholder who is affected by theproblem statement and an impactor may refer to a stakeholder thataffects the problem statement. In some embodiments, the influence maptemplate may allow the user to identify stakeholders, identifyrelationships between the stakeholders, identify the amount of control astakeholder has over the processes related to the adopted problemstatement, and identify touchpoints.

FIG. 41 illustrates an exemplary network of influence UI 4100. Thenetwork of influence UI 4100 may include an influence map 4130 includingone or more stakeholders 4132. As illustrated, each stakeholder 4132 maybe represented by a dot. The legend 4140 may identify each type ofstakeholder in the adopted problem. For example, different colors orpatterns in the dot may be indicative of different types. The legend4140 can include customer (not illustrated), employee, partner, vendor,consultant, and machine types. The network of influence UI 4100 mayinclude a searchable list of stakeholders 4136. Multiple influence maps4130 may be generated for the adopted problem statement.

The location of the stakeholder 4132 on the UI 4100 may indicate thetype and the amount of control the stakeholder has over processesrelated to the problem statement. The influence map 4130 may illustratethis using one or more control circles 4138. Although three controlcircles 4138 are illustrated, any number of control circles 4138 may beincluded without departing from the scope of this disclosure. In someexamples, a user can drag and drop an icon that represents a stakeholderinto the appropriate control circle. Stakeholders 4132 located closer tothe middle may exert more control over processes related to the adoptedproblem statement than stakeholders located closer to the edges of themap 4130. In some embodiments, the distances between the dots mayrepresent the magnitude and the strength of control that the impactorexerts over an Impactee. In some embodiments, each control circle 4138may be associated with a certain level of control defined by the user.In some embodiments, the user can define which processes related to theadopted problem statement the stakeholder controls.

The influence map 4130 can also indicate the relationship ofstakeholders 4132 to each other. As illustrated, arrows 4134 canindicate the relationship of stakeholders 4132 to each other. In someembodiments, the direction of an arrow can indicate a direction ofinfluence. For example, an assembly line employee stakeholder may beimpacted by a decision made by a financial partner stakeholder. In thisexample, an arrow 4134 may point from the financial partner stakeholderto the assembly line employee stakeholder. In some embodiments, thearrows may have different weights (e.g., thicknesses and/or color) thatindicate the impact that the influence has on the stakeholder (e.g.,three or more weights indicating a high medium or low intensity ofcontrol). In some embodiments, a thicker line may indicate a strongerimpact and/or different and/or bolder shade of similar color.

In some embodiments, the influence map 4130 may also include one or morecontrol points where a stakeholder interacts with at least one of:another stakeholder, a process, and a machine. In some embodiments, auser may provide a qualitative description of one or more controlpoints. As used herein, nodes may refer to a point of interactionbetween two or more stakeholders. As used herein, modes may refer to achannel or touchpoint a stakeholder may interface with, such as people,process user interfaces, machines, culture, environment, ambience andhearsay. Based on the visual and qualitative descriptions of control inthe influence map 4130, a user may be able to identify whichstakeholders exert the most control and the intensity of this control.As a result, this visualization can aid the system 100 and users inidentifying bottlenecks and inefficiencies, which in turn can enablerisk mitigation.

Referring back to process 3703 in FIG. 40 , in step 4003, the system 100can update the influence map 4130 based on personnel data in the seconddatabase 207. Personnel data may relate to the one or more stakeholdersand/or processes identified by the user in step 4001. The personnel datamay correspond to data that was gathered in other processes (e.g.,benchmarking, process decomposition, and KPIs). For example, personneldata gathered during the process decomposition may be used to associatestakeholders with a problem statement. In this way, the system 100 maybe able to identify missing stakeholders and identify missing linksbetween stakeholders. The updated influence map 4130 can be saved to thesecond database 207 as a new version, distinct from the initialinfluence map entered by the user in step 4001.

In step 4005, the system can use the influence map. In some embodiments,the system 100 may automatically use the updated influence map generatedin step 4003. In some embodiments, a user can accept the suggestedupdates from system 100 before using the updated influence map. Aninfluence map in accordance with embodiments of this disclosure may beused to visualize and identify causes for bottlenecks and inefficienciesin processes related to the adopted problem statement. The adoptedinfluence map may provide the user with a visual representation ofpoints and types of controls within processes that may indicateunderlying inefficiencies.

In step 4007, the system 100 can receive empathy data from the userrelated to the identified stakeholders in the adopted influence map. Insome embodiments, the degree of influence exerted by one stakeholder onanother (e.g., indicated by the arrows 4134) can inform the empathydata. In some examples, the system 100 may present the DT coordinatorwith an empathy map to input empathy data related to the identifiedstakeholders. In some embodiments, the stakeholders may access a versionof the empathy map to input their empathy data.

In some embodiments, videos of the identified stakeholders (e.g., videosof interviews of the stakeholder) may be uploaded to the system 100, andvideo-facial recognition algorithms and may be used to analyze facialexpressions. In some embodiments, audio processing and speechrecognition algorithms may be used to transcribe the interviews. In someembodiments, the system 100 can generate an empathy map based on videointerviews of a stakeholder. The video-analysis may assist the DTcoordinator in assessing and understanding the needs and emotions of thestakeholders, thereby increasing the efficiency and accuracy of theempathy maps. Using videos of interviews will be discussed in moredetail later.

FIG. 42 illustrates an exemplary empathy map UI 4200. An empathy map UI4200 may include a notes section 4268 and empathy map regions4252A-4252D. The empathy map UI 4200 may also include one or morestakeholder triggers 4256, pains 4258, barriers 4260, gains 4262, hacks4264, and delights 4266. The system 100 can generate an empathy map foreach stakeholder. In some embodiments, the system 100 can generateseparate empathy maps for different processes and different stages ofprocesses. In some embodiments, the system 100 can generate a singleempathy map that encompasses different processes and different stageswithin a process. In some embodiments, the empathy map can illustratethe experience and interactions of a stakeholder with one or more ofother stakeholders, machines, environment, and the process itself.

The empathy notes section 4268 may include notes and/or anecdotesassociated with the stakeholder 4254. The empathy map regions may beused to document the experience of a stakeholder during a processassociated with the adopted problem statement. For example, asillustrated in the figure, empathy map region 4252A can be used todocument what the stakeholder 4254 would do (e.g., the actions andactivities completed by the stakeholder 4254) during the process.Empathy map region 4252B can be used to document what the stakeholder4254 would see during the process; empathy map region 4252C can be usedto document what the stakeholder 4254 would say during the process; andempathy map region 4252D can be used to document what the stakeholder4254 would think and feel during the process. These empathy map regionsmay be based on interviews of the stakeholder 4254, direct input of thestakeholder 4254 into the system, and the like.

As illustrated in the figure, the empathy map UI 4200 may include one ormore stakeholder triggers 4256, pains 4258, barriers 4260, gains 4262,hacks 4264, and delights 4266. Stakeholder triggers 4256 may refer tofunctional and motivational needs of a particular stakeholder 4254.Stakeholder pains 4258 may refer to feelings or emotions stakeholder4254 may experience as related to performing a specific task.Stakeholder barriers 4260 may refer to deterrents experienced by astakeholder 4254 when performing certain tasks. Gains 4262 may refer tounmet stakeholder needs. Stakeholder hacks 4264 may refer to certaininformation that can circumvent stakeholder pains. Stakeholder delights4266 may refer to areas that provide comfort or ease to stakeholder4254. In some examples, triggers may include loss of users due to badexperience, pains may include slow customer service, barriers mayinclude budgetary issues, gains may include stitching various systemsinto one integrated experience center, hacks may include manual steps tokeep customer happy, and delights may include automating experience.This list is merely exemplary and other examples of triggers, pains,barriers, gains, hacks, and delights may be used without departing fromthe scope of this disclosure.

Referring back to FIG. 40 , the system 100 can generate a personajourney map (step 4009) based on the empathy map completed during step4007. As used herein, a persona can refer to an archetypal user. In someembodiments, the persona journey map or persona empathy map may be basedon one or more empathy maps generated in step 4007. A persona mayrepresent a single stakeholder or may be aggregated from two or morestakeholder empathy maps. The persona journey map may include theexperiences of the persona map across the lifecycle of a product orprocess with insights into functional, behavioral and emotional aspectsof the persona. The persona journey map may organize touchpoints,stakeholder interactions, and emotional experiences of the persona inthe context of the one or more processes related to the adopted problem.

FIG. 43 is an exemplary persona journey map 4300, in accordance withembodiments of this disclosure. As illustrated in FIG. 43 , an exemplarypersona journey map 4300 may include the feelings or emotionalexperiences 4370 of a persona throughout the different stages 4378 of aprocess related to the adopted problem statement. The persona journeymap 4300 may also include a description of the experiences 4372 duringthe different stages 4378 of a process related to the adopted problemstatement. In some embodiments, a user and/or the system 100 can flag4374 one or more emotional experiences. In some embodiments, the personajourney map 4300 may include the relationship between one or morestakeholders. For example, the relationship may be described in terms ofwhich stakeholder exerts influence or an impact on another stakeholder.Describing the relationship in this manner may provide clarity to theproblem statement and aid in prioritizing the problem statement (e.g.,sub-problem statements) and solutions.

FIG. 44 is an exemplary persona journey map 4400, in accordance withembodiments of this disclosure. As illustrated in the figure, personajourney map 4400 may also include a description of the processes and/oractivities the persona may engage in during each stage 4478. Forexample, the persona journey map 4400 may include a description ofprocesses 4480, touchpoints 4482, emotional experiences 4470, adescription of the experiences 4472, one or more flags 4474 of anexperience, one or more stakeholders 4476, pain-points 4484, andopportunities 4486. As discussed above, the system 100 may automaticallygenerate a persona journey map from one or more empathy maps.

In step 4011, the system 100 can aggregate the persona journey mapsassociated with the adopted problem into a problem statement experiencemap. The problem statement experience map may represent the problemstatement as it is experienced by one or more personas throughout thestages of the one or more processes and/or lifecycles associated withthe problem statement. In some embodiments, the persona journey map mayrepresent one or more problem statements as it is experienced by one ormore personas throughout the stages of the one or more processesassociated with the problem statement. In some embodiments, theexperience map may provide a visualization of how personas,stakeholders, and machinery overlap throughout one or more processesassociated with the problem statement. In some embodiments, the system100 can use ML to identify reoccurring opportunities and pain-points inthe experience map. In some embodiments, the system 100 may prioritizethe problem statement and/or solutions based on the ML analysis.

FIG. 45 illustrates an exemplary problem statement experience map 4500.As illustrated in the figure, the exemplary problem statement experiencemap 4500 may include a visualization of the KPIs 4526 (e.g., KPIsdetermined during process 3300) across the stages of one or moreprocesses 4578. The experience map 4500 may include a list of personas4544. In some embodiments, personas identified during step 4009 (e.g.,while generating persona empathy maps) may be organized in theexperience map 4500 based on their association with the stages of one ormore processes 4578. The exemplary experience map 4500 may furtherinclude one or more control points 4590, as identified in the adoptedinfluence map 4130 (step 4005 in process 3703). The exemplary problemstatement map 4500 may also include one or more persona triggers 4256,pains 4258, barriers 4260, gains 4262, hacks 4264, and delights 4266associated with each stage of the one or more processes 4578. In someembodiments, the experience map can include the persona journey map andinclude the actual experience (as is) of the one or more personas aswell as the experience the persona would like to have (to be).

FIG. 46 is illustrates an experience map UI 4600. Although shown asthree separate views, the experience map UI 4600 may be presented as asingle continuous webpage, e.g., via website 201. In some embodiments,the three views may be selected separately by a user for viewing. Asillustrated in the figure, experience map UI 4600 may include a list ofKPIs 4526 (e.g., KPIs determined during process 3300) across the stagesof one or more processes 4578. The experience map UI 4600 may include alist of personas 4644. In some embodiments, personas identified duringstep 4009 (e.g., while generating persona empathy maps) may be organizedin the experience map UI 4600 based on their association with the stagesof one or more processes 4678. The exemplary experience map UI 4600 mayfurther include one or more control points 4690, as identified in theadopted influence map 4130 (step 4005 in process 3703). The exemplaryexperience map UI 4600 may also include one or more persona triggers4656, pains 4658, barriers 4660, gains 4662, hacks 4664, delights 4666,and opportunities 4686 associated with each stage of the one or moreprocesses 4678. In some embodiments the experience map UI 4600 mayinclude the feelings or emotional experiences 4670 of a persona 4644throughout the lifecycle of the process 4678. In some embodiments, theexperience map UI 4600 may include a description of processes 4680,touchpoints 4682, a description of the experiences 4672, one or moreflags 4674 of an experience, one or more stakeholders 4676, andpain-points 4684. As illustrated, the pain-points 4684 can be visualizedby correlating the severity of the pain point 4684 to the size of thepain point 4684.

FIG. 47 illustrates a process lifecycle flow 4700 in accordance withembodiments of this disclosure. The process lifecycle flow 4700 caninclude a process visualization 4702 and an experiential visualization4704. The dotted lines 4706 indicate breaks between separate processes4708 of a lifecycle. Although not illustrated in process lifecycle flow4700, each process may include one or more stages. The bars 4710 may beused to visualize the experiences of one or more personas. In someembodiments, the bars may represent the feelings, emotions and/orexperience of the one or more personas. As illustrated in the figure, adifferent pattern may correspond to a different persona. In someembodiments a different color may be used to correspond to a differentpersona.

FIG. 48 illustrates a flowchart of process 3705 for brainstorming ideasusing system 100. Although the process 3705 is illustrated as includingthe described elements, it is understood that different order ofelements, additional elements, or fewer elements may be included withoutdeparting from the scope of the disclosure.

As illustrated, the process 3705 includes providing a collaborationenvironment to users (step 4801). In some examples the DT coordinatormay set up a brainstorming session for one or more users (e.g.,stakeholders). The DT coordinator may invite one or more stakeholders tojoin the brainstorming session. In some embodiments, the stakeholdersmay be identified based on the influence map adopted in step 4005 ofprocess 3703. In step 4803, the system 100 can receive priorityinformation ranking the adopted problem statement and/or sub-problemstatements.

The system 100 can receive one or more ideas from the users in thecollaboration environment (step 4805). In some embodiments, the system100 may crowdsource ideas from a number of stakeholders. The ideas maybe a solution to the problem statements ranked in step 4803. In someembodiments, the ideas may be received during a real-time collaborationsession. In a real-time collaboration session, stakeholders in variouslocations, including from around the globe can collaborate online (e.g.,via website 201) in real-time. In some embodiments, the ideas may bereceived during a passive session over a designated period of time(e.g., over a period of one to seven days). As referred to herein,during a passive session users may access and edit the collaborativeenvironment over the designated period of time. In some embodiments, thebrainstorming session may include one or more real-time sessions andpassive sessions. In some embodiments, the system 100 may use ML togroup similar ideas. In embodiments, the system can suggest one or moreideas based on a group of similar ideas. In some embodiments, users maybe able to drag and drop ideas to group similar ideas.

In step 4807, the system may receive aStrength/Weakness/Opportunity/Threats (SWOT) analysis from the users.During the SWOT analysis, the users may evaluate the strengths andweaknesses of the ideas and identify opportunities and threats relatedto the ideas. In some embodiments the system may retrieve data relatedto the problem statement (e.g., benchmarking data, capability processingdata, KPI data, influence maps, persona journey maps, experience maps,and the like) and contribute to the SWOT analysis. In this manner, thesystem 100 can fill in gaps missed by users.

In step 4809, system 100 may receive user scores rating the ideasgenerated in step 4805. For example, the ideas may be ranked by theusers. For example users may rank the ideas based on categoriesincluding, but not limited to, desirability, viability and feasibility.As used herein, desirability may refer to the ability of an idea orprototype to address the needs of a persona, stakeholder, and/or targetbusiness. Feasibility may refer to whether an idea or prototype can becompleted based on the strengths of the current target business andstakeholders. Viability may refer to whether the idea or prototype willadd value to the persona, stakeholder, and/or target business.

In some embodiments the system 100 may retrieve data related to theproblem statement (e.g., benchmarking data, capability processing data,KPI data, influence maps, persona journey maps, experience maps, and thelike) and provide a system-generated score and/or system-generatedpriority. The system 100 may use NLP and/or statistical analyses toanalyze the retrieved data and generate a system score. In this manner,the system 100 can provide an unbiased evaluation of the ideas to theusers. In some embodiments, the retrieved problem-related data mayinclude data from other, related problem statements saved in the seconddatabase 207. In some examples, the system 100 can generate a score forthe ideas generated in step 4805 based on previously existing prototypedata and video data in the second database 207 (e.g., generated fromprior brainstorming and prototyping processes).

In some embodiments, the system 100 may save versions of thecollaboration environment to the second database 207. In someembodiments, different versions may be saved for each input, after apre-determined interval of time (e.g., every three hours if a new inputis detected), after each real-time and/or passive brainstorming session,and the like. As used with respect to process 3705, an input may referto priority information, ideas, SWOT analyses, user scores, and thelike. In this manner, the system 100 may preserve a complete picture ofthe brainstorming collaboration process.

FIG. 49 illustrates an exemplary prioritization collaborationenvironment 4900 used during step 4803. The prioritization collaborationenvironment 4900 may include one or more problem statement details 4910may include an adopted problem statements 4912, goal 4914, description4916, and network of influence 4918 (described in more detail below).The problem statement 4912 may reflect the adopted problem statement. Insome embodiments, the problem statement details 4910 may present one ormore projects related to other processes performed by the system 100 forthe target business. For example, one or more benchmarking projects4922, capability modeling projects, 4924, and KPIs 4926. In someembodiments, the experience map 4600 may be used by the system 100 tosuggest a priority of the problem statements.

The prioritization collaboration environment 4900 may include aproblem-priority visualization 4930. The problem-priority visualization4930 may provide a visual way to identify and prioritize the problemstatements and/or sub-problem statements. The problem-priorityvisualization 4930 may include priority regions 4932 corresponding todifferent priority levels. Problem-priority visualization 4920 is merelyexemplary, and although four priority regions 4932 are illustrated, moreor less priority regions 4932 may be included in accordance withembodiments of this disclosure. In some examples, users can pin ideas todifferent priority regions 4932 of the problem-priority visualization4920.

FIG. 50 illustrates an exemplary brainstorming collaboration environment5000. The brainstorming collaboration environment 5000 may include oneor more ideas 5040 in idea generation region 5048, a SWOT analysisregion 5042, and a problem statement identification region 5044. Thesystem 100 can also indicate the users 5046 engaging with thebrainstorming collaboration environment 5000.

FIG. 51 illustrates an exemplary scoring environment 5100. The scoringenvironment 5100 may display one or more ideas 5140 and provide ascoring region 5158. The scoring region 5158 may include one or morecategories and one or more category scores 5160 corresponding to eachcategory 5152, idea 5140 pair. As discussed above, the categories mayinclude desirability, viability, and feasibility. A score-enteringfeature 5162 may be used by a user to enter a category score 5160. Insome embodiments, a user may select the desired category score 5160before entering the score using the score-entering feature 5162. A totalscore 5164 for each idea 5140 may be determined based on a sum of thecategory-scores of the idea 5140. In some embodiments, each user mayenter a score. In some embodiments, each user's score may be aggregatedor averaged to determine the total score. By relying on multiple userscores to determine the total score, the system 100 can reduce bias. Insome embodiments, users can collaborate to determine a score together.In some embodiments, the category-scores of each stakeholder are notrevealed to other stakeholders. In some embodiments, the category-scoresmay be shared with the DT coordinator. In some embodiments, the DTcoordinator can publish the scores to the collaboration environment.

FIG. 52 illustrates a flowchart of process 3707 for prototyping theselected ideas in process 3707 to validate the selected ideas. Althoughthe process 3707 is illustrated as including the described elements, itis understood that different order of elements, additional elements, orfewer elements may be included without departing from the scope of thedisclosure.

As illustrated, the process 3707 includes providing a prototypeenvironment to users (step 5201). The prototype environment may provideusers an environment to prototype and iterate one or more ideas selectedin process 3705. The users may include the users in process 3705. Insome embodiments, there may be additional users. In step 5203, thesystem 100 may receive inputs from the users in the prototypeenvironment. The user inputs may include one or more prototypes and/oredits to the one or more prototypes. In some embodiments, prototypes mayinclude, but are not limited to, images, flow charts, and the like. Eachprototype and subsequent edit (e.g., iteration) may be saved in thesecond database 207. In this manner, the system 100 may preserve arecord of each iteration of the prototype. By providing an environmentwith automatic versioning that highlights challenges, users may beencouraged to continually iterate and tweak prototypes.

In step 5205, the system may determine a desirability score for theprototype. This process is will be discussed in greater detail below.

In some embodiments, the system 100 may retrieve problem-related datafrom the second database 207. The problem-related data may be data inthe second database associated with the adopted problem statement.Examples of problem-related data are not limited to, but may includebenchmarking data, capability processing data, KPI data, influence maps,persona journey maps, experience maps, and the like. The system mayidentify one or more challenges based on the retrieved problem-relateddata and user inputs. For example, the one or more challenges mayinclude budget constraints, complexity of the problem, parallel ongoingproject challenges, and financial viability of the prototype. In someembodiments, the system 100 may include a financial viability scorebased on projected financial outcomes during the prototyping process.For example, system 100 could use NLP and statistical computations todetermine the financial viability score. In some embodiments, the system100 can identify resources needed to implement the prototype.

FIG. 53 illustrates an exemplary prototyping environment 5300. Theprototyping environment may include one or more ideas 5340, a workspaceregion 5370, and a toolkit region 5372. The workspace region may allowthe users to collaboratively build their prototype in a shared virtualspace. The toolkit region 5372 may include one or more tools. The one ormore tools may be, for example but not limited to, drawing tools (e.g.,for drawing one or more shapes tools), text input tools (e.g., forinputting text), note-flagging tools (e.g., for flagging and annotatingthe workspace region 5370), diagram tools (e.g., for generating adiagram), and the like.

FIG. 54 illustrates a flowchart of process 3709 for providing a roadmapfor the user to monitor progress in addressing the problem identified inthe adopted problem statement. Although the process 3709 is illustratedas including the described elements, it is understood that differentorder of elements, additional elements, or fewer elements may beincluded without departing from the scope of the disclosure.

As illustrated, the process 3709 may include receiving problem-statementrelated data (step 5401). The problem-statement related data mayinclude, for example, but is not limited to, benchmarking data,capability processing data, KPI data, influence maps, persona journeymaps, experience maps, brainstorming data, prototyping data, and thelike. In step 5403, the system 100 can generate a roadmap including oneor more tasks. Each of the tasks may correspond to a deadline in theprocess lifecycle. In some embodiments, tasks may correspond to one ormore stages of a process associated with the one or more problemstatements. In some embodiments, the tasks may correspond to a period oftime, e.g., a fiscal quarter.

In step 5405, the system 100 may determine a likelihood value associatedwith each of the tasks. The likelihood value may represent aprobabilistic likelihood that the target business can complete aparticular task by the deadline. Completion of the task may indicatethat the target business is making progress in addressing the problemidentified in the adopted problem statement.

In step 5407, the system 100 can present the roadmap and likelihoodvalues. In some embodiments, the roadmap can be presented to a user viathe website 207. In some embodiments, the system 100 can update theroadmap and likelihood values (5409) based on additional user inputs.The additional user inputs may include updated problem statement relateddata and/or progress data indicating the target business' completion oftasks.

FIG. 55 illustrates an exemplary road map 5500. As illustrated in thefigure, the exemplary road map 5500 may include one or more tasks 5580.Each of the one or more tasks may be organized by one or more ofownership 5582, category 5584, and timeline 5578. Ownership 5582 mayrefer to which stakeholders and/or teams (comprising one or morestakeholders) are responsible for completing the task. Category 5584 mayrefer to the type of task. For example, exemplary categories 5584illustrated in exemplary road map 5500 include, but are not limited to,automation, integration, data foundation, and deliver. Timeline 5578 mayrefer to a timeline for completing the tasks 5580. As illustrated, thetimeline is broken up by quarters.

Exemplary Process for Identifying Emotions from Video Interviews

As discussed above, videos of the identified stakeholders (e.g., videosof interviews of the stakeholder) may be uploaded to the system 100, andvideo-facial recognition algorithms, audio processing, speechrecognition, NLP, and the like may be used to analyze the video toidentify the emotions of the stakeholder. Unlike having, for example,the DT coordinator evaluate the video interview for the emotions of thestakeholder, the system 100 may be able to accurately, and without biasidentify the emotions of the stakeholder during the interview.

FIG. 56 illustrates a flowchart of process 5600 for generating anempathy map based on a video (e.g., video interview) of a stakeholder.Although the process 5600 is illustrated as including the describedelements, it is understood that different order of elements, additionalelements, or fewer elements may be included without departing from thescope of the disclosure.

As illustrated, the process 5600 may include receiving a stakeholdervideo interview (step 5601). The stakeholder video interview may bereceived, for example, from a DT coordinator. In some examples, thesystem 100 can receive the video interview from the stakeholder beinginterviewed, for example, via the website 201. In some embodiments, thestakeholder interview video can be saved to the second database 207.

In step 5602, the system 100 can apply an empathy model to the interviewvideo. Training the empathy model will be discussed in more detailbelow. The empathy model may include one or more analyses. For example,the analyses may include, but are not limited to, facial recognition,audio processing, speech recognition, NLP, and the like.

In step 5603, the system 100 can apply video analytics of the empathymodel to identify emotions exhibited by the stakeholder during thevideo. In some embodiments, an image of the video may be extracted at aregular interval (e.g., one image per second, one image every fiveseconds, one image every ten seconds, etc.). The image may be convertedinto a string, numerical value, pixelstring, or any suitable format. Theconverted image may be associated with an emotion. For example, theemotions may include, but are not limited to, at least anger, disgust,fear, happiness, sadness, surprise, and neutral. In step 5605, eachemotion can be timestamped to correspond to the time in the videointerview that the image corresponding to the emotion was taken.

In step 5607, the empathy model can perform speech recognition analysisto convert the words spoken by the stakeholder during the interview intoa speech text (e.g., an interview transcript). The interview transcriptmay be broken down by sentences. In some embodiments, deep learning (DL)analysis and NLP may be used to associate each sentence with an actioncategory. For example, the categories may include, but are not limitedto, doing, saying, seeing, thinking, pain, and delight. The sentencesmay be further categorized by emotion, e.g., anger, disgust, boredom,fear, happiness, sadness, surprise, and neutral. Each sentence may becategorized based on, for example, keywords files, sentiment libraries,and the like. In step 5609, each sentence can be timestamped tocorrespond to the time in the video interview that the sentence wasspoken. In some embodiments, the video analytics and speech recognitioncan be performed in parallel as illustrated in FIG. 56 .

In step 5611, the system 100 can overlay the emotions over the interviewtranscript. For example, the system 100 can align the emotions andactions based on their respective timestamps. In some embodiments, thesystem 100 may generate a mapping matrix that matches the identifiedemotion (based on an image) to an identified action (based on asentence). In some embodiments, the mapping matrix can be based onpsychological analysis data provided by a SME. The psychologicalanalysis data can map the transcript-based identified emotions andactions in step 5607 to the emotions based on the video analysis (e.g.,facial recognition) in step 5603 and perform a gap analysis to determinea resultant emotion. For example, a “happy” emotion identified in thevideo analysis outcome and a “boring process” identified in thetranscript at the same timestamp ranges may be associated with a“frustrated” resultant emotion. In this instance, for example, themapping matrix may determine that the difference in emotion results from“sarcasm,” which may indicate frustration. In this manner, the system100 can map each of identified emotions to the identified actions. Usingthis mapping, the system 100 can generate an empathy map. For example,the system may use NLP to generate text used to populate the empathy mapbased on the mapping matrix.

In some embodiments, the system can compare the empathy model generatedempathy map to an empathy map received from the DT coordinator viaempathy map UI 4200 (step 5613). In some examples, if the empathy modelgenerated empathy map varies widely from the DT coordinator's empathymap, the system 100 may recommend one or more changes to the DTcoordinator's empathy map.

FIG. 57 illustrates a flowchart of process 5700 for training an empathymodel, according to embodiments of this disclosure. Although the process5700 is illustrated as including the described elements, it isunderstood that different order of elements, additional elements, orfewer elements may be included without departing from the scope of thedisclosure.

As illustrated, the process 5700 may include receiving interview videosfrom external and internal sources (step 5701). Each of the interviewvideos may include at least on subject. In some examples, the subjectmay be a stakeholder of the target company. In some embodiments, theempathy training model may receive interview videos from one or moreexternal data providers and/or stakeholder videos stored on the seconddatabase 207. For example, stakeholder interview videos provided forgenerating an empathy map as described in process 5600 may also be usedto train and test the empathy model.

In step 5703, system 100 can use the received videos to fit the model toidentify emotions of the interview subject. In step 5705, the system canscore the empathy model based on the accuracy of the model. In step5709, the system 100 can evaluate a best fit model and select theappropriate model based on model accuracy. The selected model can beapplied in process 5600 in step 5602.

Exemplary Process for Determining a Prototype Desirability Score

As discussed above, ideas and prototypes may be assessed based on theirability to satisfy the three prongs of desirability (appearance),viability (financial), and feasibility (technical). In order to providean unbiased desirability assessment of a prototype, according toembodiments of this disclosure, a desirability scoring model may beapplied to prototypes received via the prototyping environment 5300. Insome embodiments, the desirability score may refer to the quality of theprototype and/or how well the prototype fulfills its intended purpose.In some embodiments, the desirability-scoring model may be applied toflowcharts, images, and the like.

FIG. 58 illustrates a flowchart of process 5800 for applyingprototype-scoring model to a prototype. Although the process 5800 isillustrated as including the described elements, it is understood thatdifferent order of elements, additional elements, or fewer elements maybe included without departing from the scope of the disclosure.

As illustrated, the process, 5800 may include receiving a prototype data(step 5801). The prototype data may be received, for example, via theprototyping environment 5300. The prototype data can include, but is notlimited to, flowcharts, images, and the like.

In step 5803, the system 100 can apply a prototype scoring model to theprototype data. The training the prototype scoring model will bediscussed in more detail below. In step 5803, the prototype scoringmodel can determine the desirability score. The desirability score asused herein, may refer to the quality of the prototype and/or how wellthe prototype fulfills its intended purpose.

At step 5807, the system 100 may ask whether the desirability scoregenerated by the prototype scoring disagrees with the desirability scorereceived from a user for a corresponding idea. For example, in someembodiments, the DT coordinator and/or stakeholders may enter adesirability score for an idea to prototype. If the determineddesirability score agrees with the received desirability score of theprototype, then the DT process may be permitted to proceed (step 5809)with the prototype that was scored in step 5805.

If the determined desirability score disagrees with the receiveddesirability score, then the system 100 may ask at step 5811 is lessthan or equal to an expected desirability score. The expecteddesirability score may be based on the received desirability score(e.g., from a user) or a predetermined desirability index. If thedetermined desirability score is greater than or equal to the expecteddesirability score, then the DT process may be permitted to proceed(step 5809). If the determined desirability score is greater than orequal to the expected desirability score, then the system 100 cansuggest that the user (e.g., DT coordinator) implement an alternateapproach to prototyping the solution for the idea and/or choose adifferent idea for prototyping. In some embodiments, the desirabilityscore and viability score may be evaluated based on the net presentvalue (NPV) of the prototype. The NPV may be determined based on afinancial statement created for the prototype. A viability score basedon NPV value is expected to be greater than or equal to the expectedviability score received from a user or based on predetermined viabilityindex. In some embodiments, if both computed desirability and computedviability scores are less than their expected counterpart values, theprototype may be recommended to be discarded.

FIG. 59 illustrates a flowchart of process 5900 for training aprototype-scoring model, according to embodiments of this disclosure.Although the process 5900 is illustrated as including the describedelements, it is understood that different order of elements, additionalelements, or fewer elements may be included without departing from thescope of the disclosure.

As illustrated, the process 5900 may include receiving prototype data(step 5901). The prototype data can include flowcharts, images, and thelike. In some embodiments, the prototype-scoring training model mayreceive prototype data from one or more external data providers and/orstakeholder videos stored on the second database 207. For example,prototype data provided for generating a desirability score as describedin process 5800 may also be used to train and test the prototype-scoringmodel.

In step 5903, system 100 can use the received prototype data to fit themodel to identify the desirability of the prototype data. In step 5705,the system can score the prototype scoring model based on the accuracyof the model. In step 5709, the system 100 can evaluate a best fit modeland select the appropriate prototype scoring model based on best fit.The selected prototype scoring model can be applied in process 5800 instep 5802.

Embodiments in accordance this disclosure may be directed to systems andmethods for identifying a benchmark competitor and determining aperformance score for the benchmark competitor. For example, thebenchmarking method may include identifying at least one attributeassociated with a target business, where the at least one attributecorresponds to a business industry. The benchmarking method may furthercomprise receiving performance data related to competitors of the targetbusiness, the competitors being in the business industry. Theperformance data may include the at least one attribute and performancevariables. In some embodiments, the at least one attribute and theperformance variables may be associated with the competitors. Thebenchmarking method may further comprise determining factor weights,where each of the factor weights may correspond to each performancevariable.

According to some embodiments, determining the factor weights mayinclude determining at least one factor based on the performancevariables. Determining the factor weights may further includedetermining eigenvalues corresponding to each of the factors, selectinga first set of the factors based on a factor threshold, applying afactor rotation to the first set of factors, determining at least onevariance associated with the first set of factors, and determining afirst set of the factor weights based on the factor rotation and the atleast one variance.

According to some embodiments the benchmarking method may furthercomprise determining MCDA-AHP weights, where each of the MCDA-AHPweights may correspond to each of the performance variables. In someembodiments, determining the MCDA-AHP weights may further includegenerating a pairwise comparison matrix, normalizing the pairwisecomparison matrix; and determining the MCDA-AHP weights by performing afirst operation on the normalized pairwise comparison matrix.

The benchmarking method may further include determining adaptive weightsbased on the factor weights and the MCDA-AHP weights. The benchmarkingmethod may further include applying each of the adaptive weights to eachof the performance variables for each competitor. The benchmarkingmethod may further include determining a performance score for each ofthe competitors, the performance score for each of the competitors beingbased on the adaptive weights and the performance variables, wherein theperformance score for each of the competitors is a quantizationrepresentative of a performance of a respective competitor. Thebenchmarking method may include selecting a benchmark competitor basedon the performance scores of the competitors.

In some embodiments, determining the factor weights and the determiningthe MCDA-AHP weights are in performed in parallel in the benchmarkingmethod. In some embodiments, determining the factor weights may furtherinclude generating a correlation matrix based on the performancevariables of the competitors, and removing at least one of theperformance variables, the at least one of the performance variableshaving a correlation coefficient less a correlation threshold. In someembodiments, the benchmarking method may further include pre-processingthe performance data by performing at least one selected from managingoutliers, standardizing the data, and addressing data gaps.

The methods discussed above may be implemented by a system (not shown).The system may include a server computer, a network, one or moredatabases, and one or more devices. The device(s) may be coupled to theserver computer using the network. The server computer can be capable ofaccessing and analyzing data from the database and the device(s).Embodiments of the disclosure can include any number of servercomputers, databases, networks, and devices.

The device may be an electronic device, such as a cellular phone, atablet computer, a laptop computer, or a desktop computer. The devicecan include a software (e.g., a web browser to access website 201), adisplay, a touch screen, a transceiver, and storage. The display may beused to present a UI to the user, and the touch screen may be used toreceive input from the user. The transceiver may be configured tocommunicate with the network. Storage may store and access data from theserver computer, the database(s), or both.

The server computer may be a machine such as a computer, within which aset of instructions, causes the machine to perform any one of themethodologies discussed herein, may be executed, according toembodiments of the disclosure. In some embodiments, the machine canoperate as a standalone device or may be connected (e.g., networked) toother machines. In a networked configuration, the machine may operate inthe capacity of a server or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine can be a personal computer (PC), atablet PC, a set-top box (STB), a personal digital assistant (PDA), acellular telephone, a web appliance, a network router, a switch orbridge, or any machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. The term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one of the methodologiesdiscussed herein.

The exemplary computer includes a processor (e.g., a central processingunit (CPU), a graphics processing unit (GPU), or both), a main memory(e.g., read-only memory (ROM), flash memory, dynamic random accessmemory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM),etc.), and a static memory (e.g., flash memory, static random accessmemory (SRAM), etc.), which can communicate with each other via a bus.

The computer may further include a video display (e.g., a liquid crystaldisplay (LCD) or a cathode ray tube (CRT)). The computer also includesan alpha-numeric input device (e.g., a keyboard), a cursor controldevice (e.g., a mouse), a disk drive unit, a signal generation device(e.g., a speaker), and a network interface device.

The drive unit includes a machine-readable medium on which is stored oneor more sets of instructions (e.g., software) embodying any one or moreof the methodologies or functions described herein. The software mayalso reside, completely or at least partially, within the main memoryand/or within the processor during execution thereof by the computer,the main memory and the processor also constituting machine-readablemedia. The software may further be transmitted or received over anetwork via the network interface device.

The term “machine-readable medium” should be taken to include a singlemedium or multiple media (e.g., a centralized or distributed databaseand/or associated caches and servers) that store the one or more sets ofinstructions. The term “machine-readable medium” shall also be taken toinclude any medium that is capable of storing, encoding, or carrying aset of instructions for execution by the machine and that cause themachine to perform any one or more of the methodologies of the presentinvention. The term “machine-readable medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical andmagnetic media, and carrier wave signals.

Embodiments in accordance with this disclosure may include methods andsystems directed to determining a domain score of an identifiedbenchmark competitor. The domain scoring method may include identifyingcompetitors of a target business. The competitors may include at leastone benchmark competitor and one or more non-benchmark competitors,where the competitors and the target business operate in a businessindustry. The domain scoring method may further include receivingunstructured data associated with the competitors and processing theunstructured data to produce processed data.

In some embodiments, the processing the unstructured data may includeseparating the unstructured data by domains of the target business toproduce domain-structured data associated with each of the competitorsand for each of the domains, separating the domain-structured data byleast one of: a positive sentiment and a negative sentiment, wherein theseparating the domain-structure data results in the processed data. Insome embodiments, for each of the domains, the processed data may beused to train a respective domain model. The processed data may beassociated with the one or more non-benchmark competitors. For eachdomain, domain-structured data may be applied to the respective domainmodel and the domain score for the at least one benchmark competitor canbe determined. The domain scores may be based on the domain-structureddata.

In some embodiments, the unstructured data may include qualitative data,and processing the unstructured data may include separating theunstructured data into components using NLP. The processing may furtherinclude associating the components of the unstructured data to thedomains. In some embodiments, the associating may be based on at leastone keyword identified in each of the components to produce thedomain-structured data. The processing the data may further includeassociating each component of the domain-structured data with a positivesentiment or a negative sentiment.

In some embodiments, the domain-scoring method further includesdynamically selecting the domain model based on at least one of:accuracy, the processed data, computation costs, and functionality.

In some embodiments, applying the domain-structured data may includeapplying the domain-structured data of the benchmark competitor anddetermining at least one of a positive or a negative sentimentassociated with the domain-structured data of the benchmark competitor.

Embodiments in accordance this disclosure may be directed to systems andmethods for determining an updated performance score. For example, themethod for determining the updated performance score may includeproviding assessment data, the assessment data including informationassociated with assessments of a target business and performancedrivers. The method may further include providing capability processdata, the capability process data including information associated withprocesses of the target business and the performance drivers. The methodmay further include identifying domains of the target business in theassessment data. The method may further include identifying the domainsof the target business in the capability process data. The method mayfurther include for each of the domains in the assessment data and foreach of the assessments of the target business, determining numbers ofoccurrences of the performance drivers in the assessment data. Themethod may further include for each of the domains in the capabilityprocess data and for each of the processes of the target business,determining numbers of occurrences of the performance drivers in thecapability process data. The method may further include for each of theassessments of the target business, determining assessment dataperformance driver weights, wherein the determination of each of theassessment data performance driver weight is based on an occurrencenumber of a respective performance driver for each of the assessments.The method may further include for each of the processes of the targetbusiness, determining capability process data performance driverweights, wherein the determination of each of the capability processdata performance driver weights is based on an occurrence number of arespective performance driver for each of the processes. The method mayfurther include for each of the domains and each of the performancedrivers, determining a respective assessment data aggregate performancedriver weight, wherein the determination of the respective assessmentdata aggregate performance driver weight is based on the assessment dataperformance driver weights associated with each of the performancedrivers. The method may further include for each of the domains and eachof performance drivers, determining a respective capability process dataaggregate performance driver weight, wherein the determination of therespective capability process data aggregate performance driver weightis based on the capability process data performance driver weightsassociated with each of the performance drivers. The method may furtherinclude for each of the domains and for each of the performance drivers,determining deviations between the assessment data aggregate performancedriver weights and the capability process data aggregate performancedriver weights. The method may further include determining a netdeviation based on the deviations. The method may further includedetermining an initial performance score of the target business based onthe assessments of the target business. The method may further includedetermining an updated performance score of the target business based onthe net deviation and the initial performance score of the targetbusiness.

In some embodiments, the performance drivers include at least one ofcost, quality, and time.

In some embodiments, the determining the numbers of occurrence of theperformance drivers in the assessment data comprises using NLP toidentify the performance drivers in the assessment data, and thedetermining the numbers of occurrence of the performance drivers in thecapability process data comprises using NLP to identify the performancedrivers in the capability process data.

In some embodiments, the method for determining an updated performancescore further includes identifying the processes in the capabilityprocess data.

Embodiments in accordance this disclosure may be directed to systems andmethods for identifying a solution to a problem of a target business andproviding a KPI recommendation. For example, the method may includeproviding problem data, the problem data including informationassociated with a problem of a target business. The method may furtherinclude providing pain-point and solution data, wherein the pain-pointand solution data including a pain-point and a solution associated withthe problem, and at least one of the pain-point and the solution isprovided by a subject matter expert. The method may further includecomparing the problem data to the pain-point and solution data, whereinthe comparing the problem data to the pain-point and solution datacomprises matching the pain-point to the problem. The method may furtherinclude identifying the solution associated with the pain-point, thesolution associated with the problem matched to the pain-point. Themethod may further include determining whether or not the solution isassociated with a KPI, the KPI being at least one of: a solution KPI anda pain-point KPI. The method may further include providing arecommendation of the KPI, when the solution is associated with the KPI.The method may further include forgoing the recommendation of the KPI,when the solution is not associated with the KPI.

In some embodiments, the comparing the problem data and the pain-pointand solution data further comprises using NLP to match the pain-point tothe problem.

In some embodiments, the method further includes determining whether ornot an effectiveness of the solution to the problem is below aneffectiveness threshold. The method may further include disassociatingthe solution with the problem, when the effectiveness of the solution tothe problem is below the effectiveness threshold. The method may furtherinclude forgoing the disassociating the solution with the problem, whenthe effectiveness of the solution to the problem is not below theeffectiveness threshold.

In some embodiments, the solution is associated with a domain of thetarget business. The method may further include presenting the solution.The method may further include receiving an input to filter out thepresentation of second information associated with the domain. Themethod may further include in response to the receiving the input tofilter out the presentation of information associated with the domain,ceasing the presentation of the solution.

Embodiments in accordance with this disclosure may include methods andsystems directed to providing a target business a platform to enablestakeholders, (e.g., users) in the target business to perform a DTprocess. The DT method may include generating an influence map.Generating the influence map may include receiving first data, whereinthe first data identifies individuals associated with the problemstatement, receiving second data, wherein the second data identifies arelationship between at least one of the individuals, at least oneprocess, and at least one machine, where the at least one process andthe at least one machine are associated with the problem statement.

The DT method may further include updating the influence map based onthird data, the third data associated with a target business. The DTmethod may further include receiving empathy data related to theindividuals, where the empathy data is associated with at least oneemotion of the at least one of the individuals. In some embodiments, theempathy data may be received during a lifecycle of the at least oneprocess, where the at least one process associated with the problemstatement. The DT method may include generating two or more persona mapsbased on the empathy data and the updated influence map, wherein the atleast one persona map traces at least one selected from the at least oneemotion, experiences, touchpoints, pain-points, and opportunities of apersona during the lifecycle of the at least one process. The DT methodmay further include generating an experience map based on the two ormore persona maps, wherein the experience map aggregates the two or morepersona maps.

The DT method may further include receiving at least one idea associatedwith a solution to the problem statement via a collaborationenvironment. The DT method may further include receiving an evaluationof the at least one idea, where the evaluation comprises at least oneof: a desirability score, a viability score, and a functionality score.The DT method may further include prototyping the at least one idea andproviding a roadmap. The roadmap may be configured to identify at leastone task associated with the at least one process associated with theproblem statement, identify one or more of the individuals from thefirst data to complete the at least one task, and associate the at leastone task with a likelihood value, wherein the likelihood value may bebased on a likelihood that the identified one or more individualscomplete the at least one task.

In some embodiments, the generating the two or more persona maps of theDT method may include receiving at least one video interview of the atleast one of the individuals, identifying at least one emotion usingvideo-facial recognition, and associating the at least one emotion withat least one point of the lifecycle.

In some embodiments, the DT method may further include generating asecond evaluation of the at least one idea, wherein the secondevaluation may be based on fourth data, wherein the fourth data isanalyzed using NLP.

In some embodiments, generating the influence map of the DT process mayfurther include receiving an indication of a direction of influencebetween individuals from the first data, receiving an indication of anintensity of influence between individuals from the first data, andreceiving an indication of intersections of individuals from the firstdata, machines associated with the problem statement, and processesassociated with the problem statement, wherein the indication theintersections includes the direction of influence and the intensity ofinfluence. In some embodiments, the updating the influence map mayfurther include comparing the generated influence map to the third data,the third data related to at least one of the individuals, machines, andprocesses associated with the problem statement.

In some embodiments generating the influence map of the DT process mayfurther include identifying first impactor individuals, where animpactor individual may be associated with the direction of influenceoriginating at the impactor individual, and second identifying impactorindividuals based on the intensity of influence between individuals fromthe first data. In some embodiments, the influence map may be adapted tohighlight inefficiencies in a target business.

Although the disclosed examples have been fully described with referenceto the accompanying drawings, it is to be noted that various changes andmodifications will become apparent to those skilled in the art. Forexample, like numbers connote like features. Although some modificationsmay described with respect to particular examples, one skilled in theart will understand that the same modifications may be applied to otherembodiments though not described with particularity. Such changes andmodifications are to be understood as being included within the scope ofthe disclosed examples as defined by the appended claims.

The invention claimed is:
 1. A method for evaluating a performance of atarget business with a computer system, the method comprising:receiving, by the computer system, problem data, the problem dataincluding information associated with a first problem of the targetbusiness associated with a first industry; receiving, by the computersystem, pain-point and solution data, wherein: a first portion of thepain-point and solution data is associated with the first problem andthe first industry, and a second portion of the pain-point and solutiondata is associated with a second problem and a second industry; storing,by the computer system, the pain-point and solution data in a database;determining, using a machine learning model, an applicability of thesecond portion of the pain-point and solution data to the firstindustry; updating the pain-point and solution data in the databasebased on an output of the machine learning model, the output of themachine learning model indicative of the applicability; comparing, bythe computer system, the problem data to the updated pain-point andsolution data by matching one or more pain-points of the updatedpain-point and solution data to the first problem; identifying, by thecomputer system, one or more solutions associated with the one or morematched pain-points; determining, by the computer system, whether theone or more solutions are associated with a key performance indicator(KPI), the KPI being at least one of: a solution KPI or a pain-pointKPI; providing, by the computer system, a recommendation of the KPI whena solution of the one or more solutions is associated with the KPI;forgoing, by the computer system, the recommendation of the KPI when theone or more solutions are not associated with the KPI; generating, bythe computer system, a customized user interface (UI) based on theupdated pain-point and solution data and the recommendation of the KPI;presenting, by the computer system, the customized UI to a userassociated with the target business, wherein the customized UI displaysa single visualization comprising the one or more matched pain-points,the one or more solutions, and the recommended KPI; receiving, by thecomputer system, an indication of an effectiveness of the one or moresolutions or the recommended KPI; and updating, by the computer system,the database, and the customized UI based on the received indication. 2.The method of claim 1, wherein the comparing the problem data to theupdated pain-point and solution data further comprises using naturallanguage processing (NLP) to match the one or more pain-points to thefirst problem.
 3. The method of claim 1, further comprising: determiningwhether an effectiveness of the solution of the one or more solutions tothe first problem is below an effectiveness threshold; disassociatingthe solution of the one or more solutions with the first problem, whenthe effectiveness of the solution of the one or more solutions to thefirst problem is below the effectiveness threshold; and forgoing thedisassociating the solution of the one or more solutions with the firstproblem, when the effectiveness of the solution of the one or moresolutions to the first problem is not below the effectiveness threshold.4. The method of claim 1, wherein the solution of the one or moresolutions is associated with a domain of the target business, the methodfurther comprising: presenting the solution of the one or more solutionsto a user associated with the target business; receiving an input tofilter out presentation of information associated with the domain; andin response to the receiving the input to filter out the presentation ofinformation associated with the domain, ceasing the presentation of thesolution of the one or more solutions.
 5. The method of claim 1, furthercomprising: determining an initial performance score based onassessments of the target business; determining a net deviation based ona deviation between assessment data and capability process dataassociated with the target business, wherein: the assessment datacomprise information associated with the assessments of the targetbusiness and performance drivers, and the capability process datacomprise information associated with processes of the target businessand the performance drivers; and determining an updated performancescore of the target business based on the net deviation and the initialperformance score, wherein the solution is identified further based onthe updated performance score.
 6. The method of claim 1, furthercomprising: in response to an input requesting a domain score for acompetitor of the target business: receiving an input identifying thecompetitor of the target business; determining the domain score for thecompetitor based on separated domain-structure data associated with thecompetitor; and providing the domain score.
 7. The method of claim 1,wherein the first portion of the pain-point and solution data isprovided by a subject matter expert.
 8. The method of claim 1, whereinthe matching the one or more pain-points of the updated pain-point andsolution data to the first problem comprises: determining a similarityscore based on a pain-point of the updated pain-point and solution dataand the first problem; comparing the similarity score to a predeterminedsimilarity threshold; when the similarity score is above thepredetermined similarity threshold, associating the pain-point of theupdated pain-point and solution data with the first problem; and whenthe similarity score is below the predetermined similarity threshold,forgoing associating the pain-point of the updated pain-point andsolution data with the first problem.
 9. The method of claim 8, furthercomprising inputting the one or more matched pain-points of the updatedpain-point and solution data and the first problem into the machinelearning model to determine the similarity score.
 10. The method ofclaim 1, wherein the pain-point and solution data further comprises afunction and a root cause.
 11. The method of claim 10, furthercomprising identifying the function based on one or more predeterminedfunctions associated with the pain-point.
 12. The method of claim 10,further comprising identifying the root cause based on one or morepredetermined root causes associated with the function.
 13. The methodof claim 1, further comprising in response to providing therecommendation of the KPI, generating a user interface that includes theone or more pain-points of the updated pain-point and solution data, theone-or more solutions associated with the one or more matchedpain-points, the recommended KPI, and a visual representation of therecommended KPI.
 14. The method of claim 1, further comprisingdetermining, using the machine learning model, a new KPI solution whenthe one or more solutions are not associated with the KPI.